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    • 专利摘要:
    Strata surfaces can be identified in well logs and correlated through well logs taking into account manual corrections. For example, a computing device may receive well logs. The computing device can determine multiple stratum-surface correlations based on the well logs. Then, the computing device can receive an input from the user indicating a correction at a given stratum-surface correlation. Based on the correction of the given stratum-surface correlation, the computing device can update some or all of the other stratum-surface correlations.
    公开号:FR3063765A1
    申请号:FR1850816
    申请日:2018-01-31
    公开日:2018-09-14
    发明作者:Yang Peng；Ahinoam POLLACK；Kainan WANG；Ming-Kang SHIH；Krisha Hansel TRACY；Jesse Mathias Lomask
    申请人:Landmark Graphics Corp；
    IPC主号:
    专利说明:

    © Publication no .: 3,063,765 (to be used only for reproduction orders)
    ©) National registration number: 18 50816 ® FRENCH REPUBLIC
    NATIONAL INSTITUTE OF INDUSTRIAL PROPERTY
    COURBEVOIE © Int Cl 8 : E 21 B 47/00 (2017.01), G 06 Q 50/02, G 06 F 17/50
    A1 PATENT APPLICATION
    ©) Date of filing: 31.01.18. © Applicant (s): LANDMARK GRAPHICS CORPORA- © Priority: 08.03.17 IB WOUS2017021361. TION -— US. @ Inventor (s): PENG YANG, POLLACK AHINOAM, WANG KAINAN, SHIH MING-KANG, TRACY KRISHA (43) Date of public availability of the HANSEL and LOMASK JESSE MATHIAS. request: 14.09.18 Bulletin 18/37. ©) List of documents cited in the report preliminary research: The latter was not established on the date of publication of the request. (© References to other national documents ® Holder (s): LANDMARK GRAPHICS CORPORA- related: TION. ©) Extension request (s): (© Agent (s): GEVERS & ORES Société anonyme.
    CORRELATION OF THE SURFACES OF STRATES THROUGH WELL LOGS.
    FR 3 063 765 - A1 _ The strata surfaces can be identified in well logs and correlated through well logs taking into account manual corrections. For example, a computing device can receive well logs. The computing device can determine multiple stratum-surface correlations based on the well logs. Then, the computing device can receive input from the user indicating a correction to a given stratum-surface correlation. Based on the correction of the given stratum-surface correlation, the computing device can update some or all of the other stratum-surface correlations.
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    2016-IPM-100868-U1-FR 1
    CORRELATION OF THE SURFACES OF STRATES THROUGH WELL LOGS
    Technical area
    This disclosure generally relates to methods and devices used in geological interpretation. More specifically, but in no way limiting, this disclosure relates to the correlation of strata surfaces across well logs.
    Context
    A well system (eg, for gas and oil extraction) can include multiple wells dug through an underground formation. Each wellbore may include a well logging tool which provides data in the form of well logs to a well operator. A well log can be a record that indicates the geological formations that are penetrated by a wellbore. The well operator can manually examine the well logs to identify strata or other features of interest in the underground formation. For example, the well operator can manually examine multiple well logs in a two-dimensional (2D) cross-sectional view or a three-dimensional (3D) view to identify structures or features of interest.
    Brief description of the figures
    Figure 1 is a side cross-sectional view of an example of a well system for obtaining well logs according to certain aspects.
    Figure 2 is a graph of an example of strata surfaces that have been correlated among multiple well logs in some aspect.
    Figure 3 is a graph of an example of correlation on strata surfaces of Figure 2 according to certain aspects.
    Figure 4 is a graph of an example of an updated stratum surface from the correlations illustrated in Figure 3 according to certain aspects.
    Figure 5 is a flow diagram of an example of a method for correlating strata surfaces across well logs in certain aspects.
    FIG. 6 is a flow diagram of an example of a method for determining an updated version of a stratum surface based on a correction according to certain aspects.
    2016-IPM-100868-U1-FR 2
    FIG. 7 is a flow diagram of an example of a method making it possible to carry out dynamic time alignment according to certain aspects.
    Figure 8 is a graph of an example of two well logs with nodes correlated to each other in certain aspects.
    Figure 9 is a graph of an example of an error matrix according to certain aspects.
    FIG. 10 is an enumeration of an example of a pseudo code making it possible to generate an error matrix accumulated according to certain aspects.
    Figure 11A is a graph of an example of sub-paths through each of the sub-error matrices of Figure 9 according to certain aspects.
    Figure 11B is a graph of an example of a path completed through the error matrix of Figure 9 according to certain aspects.
    FIG. 12A is a perspective view of an example of a layer surface generated using a least squares optimization according to certain aspects.
    FIG. 12B is a perspective view of an example of a stratum surface generated by the propagation of well-log correlations according to certain aspects.
    Figure 13 is a flow diagram of an example of a computing device in certain aspects.
    detailed description
    Certain aspects and characteristics of this disclosure relate to the updating of a group of stratum-surface correlations based on a manual correction of a stratum-surface correlation given within the group. A stratum-surface correlation can be an association between nodes (eg, data points) in well logs that correspond to the same surface of a given stratum within an underground formation. For example, a computing device can receive multiple well logs. The computing device can analyze the well logs to determine a group of strataurface correlations. After which, the computing device can receive input from the user indicating a manual correction to one of the stratum-surface correlations in the group. Depending on the manual correction, the computing device can update some or all of the other stratum-surface correlations in the group. For example, based on a manual correction on a first stratum-surface correlation in the group, the computing device can update a second stratum-surface correlation in the group.
    2016-IPM-100868-U1-FR 3
    More specifically, the strata in an underground formation are laid directly on top of each other, so that an upper surface of one stratum forms a lower surface of another stratum. This leads to an interdependence among the stratum surfaces, and consequently the stratum-surface correlations. The IT system can take this interdependence into account by recalculating the stratum-surface correlations in the group using manual correlation as a constraint. This can give a more realistic and precise determination of the stratum-surface correlations in the group.
    These illustrative examples are given in order to present to the reader the general object described here and they are not intended to limit the scope of the concepts disclosed. The following sections describe various other features and examples with reference to the figures in which identical numbers denote identical elements, and the direction descriptions are used to describe the illustrative aspects but, like the illustrative aspects, should not be used to limit the present description .
    Figure 1 is a side cross-sectional view of an example of a well system for obtaining well logs in certain aspects. The well system 100 includes multiple wells 102a-h dug through an underground formation 104. The wells 102a-b extend from the surface of the well 108 in the strata 106a-c of the underground formation 104. strata 106a-c may include different materials (eg, rock, earth, oil, water or gas) with different thickness and shape.
    Some or all of your wellbores 102a-h may include well tools, such as logging tools 110a-b, for generating well logs. Each of the well tools can measure properties of rocks, fluid, or other contents of strata 106a-c and use the measured properties to generate a respective well log. For example, the logging tool 110a can measure the electrical, acoustic, radioactive, electromagnetic or pressure properties of the regions of the strata near the wellbore 102a. The logging tool 110a can then use the measurements to generate a well log. A separate well log can be generated for each of the wellbores 102a-h.
    The well tools can electronically transmit the well logs to a computer device 112, which can be positioned on site (illustrated in Figure 1) or off site. The well tools can communicate electrically with the computing device 112 through a wireless or wired interface. In some examples, well tools can electronically transmit well logs
    2016-IPM-100868-U1-EN 4 to computer device 112 indirectly, e.g., over the Internet or another network.
    The computing device 112 can display some or all of the well logs in the form of two-dimensional (2D) or three-dimensional (3D) figures. An example of such a figure is illustrated in Figure 2. In Figure 2, each vertical line represents a single wellbore. For example, the vertical line 202a represents a given wellbore. Since there are 21 vertical lines, there are 21 different boreholes shown in this figure. The part of the line 202a near the top 206 of the figure represents the part of the wellbore close to the surface of the well, the depth of the wellbore increasing towards the bottom of the page. The patterned data on the left of a vertical line is a well log. For example, patterned data 204 is a well log corresponding to the wellbore represented by line 202a. A peak, a trough, or a change in shading in the pattern data 204 can represent the changing composition of the strata in the underground formation.
    In Figure 2, the nine rightmost vertical lines have two corresponding well logs, a well log on the left and another well log on the right. For example, the vertical line 202b has a well log 210 on the left and another well log 212 on the right. These well logs come from the same wellbore (represented by the vertical line 202b), but generated with different processes. For example, well log 210 can be generated based on the gamma radiation emitted by strata in the underground formation, and well log 212 can be generated based on the spontaneous potential of strata in the underground formation. The computing device 112 can produce any number and combination of well logs individually or simultaneously.
    In the example shown in Figure 2, all well logs are from wells that are part of the same well system and penetrate the same strata. And so, all well logs provide information for the same strata. Because the strata are not uniform, the wells are located in different locations within the well system, and the wells are different depths, the well logs do not line up perfectly the ones with the others. For example, the grooves and hollows representing a given stratum in the well log 208 may be closer to the top 206 of FIG. 2 than the grooves and hollows representing the same stratum in
    2016-IPM-100868-U1-EN 5 well log 210. It may be desirable to identify and correlate a single stratum across multiple well logs.
    Typically, a geologist can review the well logs and manually identify the same stratum in each well log. The geologist can then correlate the same stratum through the well logs. Examples of strata identified and correlated manually are given as solid lines in Figure 2. The first solid line (extending from box "1" on the left) can represent the upper surface of a first layer. The second solid line (extending from box "2" on the left) can represent the upper surface of a second layer. The third solid line (extending from box "3" on the left) can represent the upper surface of a third layer. And, the fourth solid line (extending from the box "4" on the left) can represent the upper surface of a fourth layer. Manual examination and correlation of strata surfaces among multiple well logs can be costly, time consuming, difficult and subjective. A geologist can spend several days identifying and correlating thousands of surfaces through dozens of well logs.
    In some examples, the computing device 112 can run a software application to identify and correlate strata across multiple well logs. For example, the computing device 112 can use the software application to analyze each of the well logs, identify the various strata in the well logs and correlate the strata with each other, all without manual intervention. Examples of strata surfaces correlated using the software application are illustrated as dotted lines in Figure 2. The first dotted line (extending from box "1" on the left) can represent the upper surface of a first layer, The second dotted line (extending from the box "2" on the left) can represent the upper surface of a second layer. The third dotted line (extending from box "3" on the left) can represent the upper surface of a third layer. And, the fourth dotted line (extending from the box "4" on the left) can represent the upper surface of a fourth layer. Correlating strata using the software application can be faster, more consistent, require less processing time, and require less power than manual correlation.
    Software-based correlation, however, does not always provide the same results as manual correlation. For example, the dotted lines in Figure 2 deviate from solid lines at certain locations. These deviations
    2016-IPM-100868-U1-FR 6 are represented by arrows. Some exempt from this disclosure allow a user to correct these deviations or other errors in the surfaces of the correlated strata using the software application. For example, the computing device 112 may receive input from the user in which the user drags a portion of a surface of an identified stratum using the software application to a new location. An example of this sliding is represented by your arrows in figure 3. In figure 3, your surfaces of stratum 1 and stratum 3 correlated by the software (illustrated by dotted lines) are dragged towards certain locations by (' user to more precisely conform to manually correlated surfaces.
    In certain examples, the computer device 112 can update one or more surfaces of strata by taking account of the aforementioned manual corrections. For example, manual corrections on the surfaces of stratum 1 and stratum 3 can also affect a surface of stratum 2, because stratum 2 is positioned between stratum 1 and stratum 3. Consequently, the computing device 112 can update a surface for stratum 2 using manual corrections on stratum 1 and stratum 3 as constraints. An example of an updated surface for stratum 2 is illustrated in figure 4. In figure 4, line 402 is the original surface of stratum 2 identified and correlated by the software application. Line 406 represents the surface manually identified for stratum 2. And line 404 represents the updated surface for stratum 2, which was generated by the software application and taking into account the manual corrections on stratum 1 and stratum 3 Since the updated surface is closer to the manually identified surface, the updated surface could be more precise.
    In some exemptions, the computing device 112 may implement some or all of the above features using the method illustrated in Figure 5. In other examples, the computing device 112 may implement a method that has more steps, fewer of steps, different steps, or a different order of steps illustrated in Figure 5.
    Referring now to Figure 5, in block 502, the computing device 112 determines the boreholes of interest. The computing device 112 can determine the wellbore of interest based on the user input indicating your wellbore of interest. For example, the computing device 112 may display a graphical user interface (GUI) having a visual representation of multiple wellbores in a well system. The computing device 112 can
    2016-IPM-100868-U1-EN 7 receive, in the form of user input, a selection of some or all of the boreholes displayed in the GUI. In some examples, the boreholes do not need to be chosen or ordered along a sedimentary slope line. The computing device 112 can receive user input through a user input device. The computing device 112 can use the wells chosen as the wells of interest.
    In block 504, the computing device 112 receives well logs associated with the boreholes of interest. For example, the computing device 112 can retrieve the well logs from a local memory device. In another example, the computing device 112 can communicate with a remote server to retrieve the well logs from a remote database.
    In block 506, the computer device 112 preprocesses the well logs corresponding to the boreholes of interest. The computing device 112 can perform one or more operations to preprocess the well logs.
    In some examples, the computing device 112 can preprocess the well logs by standardizing the well logs so that your well logs can be compared to each other. For example, all well logs can have a uniform range of values. In order to make the well logs comparable, the computing device 112 can manipulate the well logs so that all the well logs have the same range of values. As a particular example, the spontaneous potential well log generally does not have a range of standard values. Therefore, in order to compare the spontaneous potential well logs, the computing device 112 can normalize the two well logs so that both have the same range of values.
    In some exemptions, the computer device 112 can preprocess the well logs by smoothing the well logs. For example, well logs can have high frequency noise. The computing device 112 can smooth the well logs to reduce or eliminate the influence of high frequency noise. For example, the computing device 112 may divide the well log into intervals having a predetermined amount of nodes (e.g., 5-node intervals). The computing device 112 can determine an average value of the nodes in an interval and assign the average value to all the nodes in the interval. The computing device 112 can repeat this process for all intervals, thereby smoothing the well log. This can help reduce the influence of high frequency noise in the well-log correlation process.
    2016-IPM-100868-U1-FR 8
    In some exemptions, the computing device 112 can preprocess your well logs by removing a trend from the well logs. A trend may include an increase or decrease in the values of a well log with depth. A trend may occur due to a malfunction of the well tool that generated the well log or certain other non-geological phenomena. The computing device 112 can remove a trend from a well log to reduce errors associated with the trend.
    In some exemptions, the computer device 112 may not preprocess your well logs. Instead, the computing device 112 may use the well logs in their raw form to perform some or all of the remaining steps described below.
    In block 508, the computing device 112 identifies and correlates your strata among the pretreated well logs. For example, the computing device 112 may execute a software application which automatically (eg, without manual intervention) identifies strata in the well logs and correlates the strata through the well logs.
    In block 510, the computing device 112 determines whether a manual correction on a given surface of strata has been received. Manual correction can be provided by a user.
    In some examples, the computing device 112 may receive input from the user indicating manual correction. For example, the computing device 112 may receive text entry designating a new location for a stratum surface and indicating that the originally identified location was incorrect. This can be manual correction. As another example, the computing device 112 may display a line of GUI indicating a location of a stratum surface. The computing device 112 can detect a user who is dragging the line from the GUI to a new location, thereby indicating that the original location of the stratum surface was incorrect. This can be manual correction.
    If the computing device 112 determines that a manual correction to a stratum surface has been received, the process can continue to block 512. Otherwise, the process can continue to block 514. In block 514, the computing device 112 displays a GUI, such as the GUI illustrated in Figures 2 to 4, visually representing the surfaces of the stratum.
    In block 512, the computing device 112 determines an updated version of one or more other stratum surfaces based on the correction
    2016-IPM-100868-U1-FR 9 manual. For example, the computing device 112 can determine an updated version of a single stratum surface or updated versions of multiple stratum surfaces based on manual correction. In some examples, the computing device can again determine all of the stratum surfaces based on manual correction. This can lead to the non-modification of certain stratum surfaces and the modification of other stratum surfaces.
    In some examples, the computing device 112 can determine the updated version of the other stratum surface (s) using the method illustrated in Figure 6. In other examples, the computing device 112 can implement a method that has more steps, fewer steps, different steps, or a different order of steps illustrated in Figure 6.
    In block 602, the computing device 112 determines multiple well-log pairs among the well logs (eg, received in block 504). In some examples, the computing device 112 can determine all possible log well pairs from the well logs. For example, the computing device 112 can determine a total of (njoumaux * injournaux-1)) / 2 pairs of well journals, in which njoumaux represents the number of well journals. As a specific example, if there are a total of 10 well logs, the computing device 112 can generate (10 * (101)) / 2 = 45 separate well-log pairs.
    In block 604, the computing device 112 performs dynamic time alignment on each well-log pair to determine a respective correlation between each well-log pair. Dynamic time alignment can be a technique for determining an alignment or correlation between two time-dependent sequences that may vary in speed. A result of dynamic time alignment may include a set of correlation points between the two well logs in the well-log pair. For example, the computing device 112 may use dynamic time alignment to determine that the points [1, 2, 3, 4, 5] in a well log are correlated to the points [3, 4, 5, 6, 7, 9] in another well log.
    The computing device 112 can use manual correction from block 510 to perform dynamic time alignment. For example, referring again to Figure 3, manually updating the location of the surface of stratum 1 in well log 304 (illustrated by the arrow) can give an updated correlation between the surface of the stratum 1 in well log 304 and the surface of stratum 1 in well log 306. Manual updating of
    2016-IPM-100868-U1-EN 10 the location of the surface of stratum 3 in the well log 302 can give an updated correlation between the surface of stratum 3 in well log 302 and the surface of the stratum 3 in the well log 304. The computing device can use the updated correlations coming from the manual corrections to carry out the dynamic time alignment.
    Another free manual correction is illustrated in Figure 8. Node number 7 in the well log for Well I is a manually corrected location for the surface of stratum 1 and it is correlated with node number 4 in the log well for te Well J. Node number 18 in the well log for te Well I is a manually corrected location for the surface of stratum 3 and it is correlated to node number 15 in the well log for well J. Les manual corrections give updated correlations for the surface of stratum 1 and the surface of stratum 3. The computing device 112 can use the updated correlations coming from manual corrections to perform dynamic time alignment.
    In some examples, the computing device 112 can perform dynamic time alignment on a pair of log wells according to the method illustrated in FIG. 7. In other examples, the computing device 112 can implement a method that has more steps, fewer steps, different steps, or a different order of steps illustrated in Figure 7.
    In block 702, the computing device 112 divides the well logs in the well-log pair into a subsection based on an updated correlation resulting from a manual correction (eg, an updated correlation determined in block 604). The number of subsections can be equal to the number of updated correlations identified in block 604 + 1. For example, referring to Figure 8, there are two updated correlations due to two manual corrections, your two updated correlations giving three subsections 802, 804 and 806. The computing device 112 can determine and divide the well logs into these three subsections 802, 804 and 806. As is presented in more detail below, the computing device 112 can carry out several smaller (and more precise) correlations using your small subsections for Well I and Well J, rather than correlating, in their entirety, your well logs for Well I and Well J.
    In block 704, the computing device 112 selects a sub-section. For example, the computing device 112 can select the first subsection from a list of subsections generated in block 704. In some
    2016-IPM-100868-U1-EN 11 examples, two or more subsections can be chosen and analyzed in parallel.
    In block 706, the computing device 112 generates an error sub-matrix for the sub-section. The computing device 112 generates the error sub-matrix from a larger error matrix. An example of error sub-matrices and the larger error matrix 900 is illustrated in Figure 9. An axis of the error matrix 900 can represent nodes in a well log (eg, the well I in Figure 8) and another axis of the error matrix 900 may represent nodes in another well log (eg, well J in Figure 8). Each box in the error matrix 900 can represent a correlation between a node in a well log and another node in another well log. The computing device 112 can generate the error matrix 900 using the following equation:
    ErrorMatrix (i, j) = Logfi) - Logj (j) which can give an error matrix 900 of the size i x j.
    Examples of the updated correlations in FIG. 8 are illustrated in the error matrix 900 of FIG. 9 in the form of points 902 and 904. These updated correlations can be used as constraints which significantly reduce the number of possible trips from the corner. lower left of the error matrix 900 to the upper right corner of the error matrix 900. For example, your possible paths from the lower left corner of the error matrix 900 (at point (0,0)) to point 902 are inside the area designated as error sub-matrix 1. Possible routes from point 902 to point 904 are inside the area designated as error sub-matrix 2. The possible paths from point 904 to the upper right corner of the error matrix 900 (at point (24,24)) are inside the area designated as error sub-matrix 3. The computing device 112 can perform an alignment dynamic temporal using each of these error sub-matrices, which are much smaller than the entire error matrix 900. The dynamic temporal alignment gives a set of points (nodei, nodej} which can form the minimum average error path from the lower left corner of the error matrix 900 to the upper right corner of the error matrix 900 through manually updated correlation points 902, 904.
    In some examples, a well log may have two or more sets of data generated using different methods. For example, a data log may have data sets of gamma or sonic radius. The computing device 112 can generate a combined error matrix from two
    2016-IPM-100868-U1-EN 12 or more data sets. For example, the computing device 112 can generate a first error matrix for the sonic data set and a second error matrix for the gamma ray data set. The computing device 112 can then calculate the average of the first error matrix and the second error matrix, or otherwise combine the first error matrix and the second error matrix, to generate a combined error matrix .
    In some examples, averaging the error matrices as presented above may result in inaccuracies due to the fact that your error sub-matrices may have distributions of different values. For example, an error sub-matrix generated from a sonic data set may have a different distribution of values compared to another error sub-matrix generated from a gamma ray data set. . In some examples, the computing device 112 can overcome this problem by transforming the error sub-matrices from the different data sets into respective uniform distributions before combining the error sub-matrices. The transformed error sub-matrices can then be combined. For example, the computing device 112 can implement the following equation:
    CombinedEMi j = Uniform (SEM data setA Xj + Uni f orm ( SEM data in which CombinedEMij is a combined error sub-matrix;
    SEM data _ setA is an error sub-matrix generated using a data set (data set A); SEM datasetB is an error sub-matrix generated using another dataset (dataset B); and Uniform describes an operation to transform an error sub-matrix into a uniform distribution.
    In block 708, the computing device 112 generates an accumulated error matrix for the sub-section based on the error sub-matrix. In some examples, the computing device 112 can generate the error matrix accumulated according to the method illustrated in the pseudo code illustrated in FIG. 10. Referring to your FIG. 10, AccEij can be a matrix which stores your values of accumulated error. ErrMij can be the error sub-matrix previously created from block 706. PathLij can be the matrix that stores the minimum average error path length for each element in the accumulated error matrix. Dir t j can be a matrix that stores a record of the last step that was taken to get to each element in the path. Storing a record of the last step taken to get to each item in the path can simplify a backtracking process (described in more detail ci3063765
    2016-IPM-100868-U1-EN 13 below) which can follow the creation of the accumulated error matrix. Referring to the pseudo code in Figure 10, if the element is a possible final element in the path, we can give a value of 0 to the element in Dir ^. If the last step that was taken came from a left element, we can give a value of 1 to the direction in Dirij. If the last step which was taken came from a lower left element, we can give a value of 2 to the direction in If the last step which was taken came from a bottom element, we can give a value of 3 to management in Dirij.
    The method illustrated in the pseudo code of FIG. 10 includes a walking constraint. The walking constraint requires that for every two steps in the horizontal direction, one vertical step must be taken. And, for every two steps in the vertical direction, one horizontal step must be taken. Others may not contain the margin constraint or may include a different version of the market constraint.
    The characteristics of the process illustrated in Figure 10 may change depending on the characteristics of the subsection. For example, subsection 802 in Figure 8 has an undetermined start node and a known end node (node number 7). Subsection 804 has a known start node (node number 7) and a known end node (node number 18). Sub-section 806 has a known starting node (node number 18) and an unknown ending node. In certain examples, the part 1002 of the pseudo code of FIG. 10 can be replaced by a different pseudo code (ef the method carried out by the computer device 112 modified accordingly) based on the fact that the start node and the end node of the subsection are known or unknown. For example, if the subsection has a known start node and a known end node, part 1002 can be replaced by the following pseudo code:
    f or (i = 0, j # = 0)
    AccE (0, j) = oo for (i # 0, j = 0)
    AccE (i, 0) - co f or (i - i max , j Φ jmax)
    AccE (i max , j) = oo for (i * i max , j * j max )
    AccE (i, j max ) = oo
    As another example, if the subsection has a known start node and an unknown end node, part 1002 can be replaced by the following pseudo code:
    2016-IPM-100868-U1-FR 14 for (i = 0, j Ψ 0)
    AccE (Q, f) = oo for (i f0, j = 0)
    AccE (i, 0) = oo
    As another example, if the subsection has an unknown start node and a known end node, part 1002 can be replaced by the following pseudo code:
    f or (ï - i max , j Φ jmax)
    AccE (i max , j) = oo for (î Φ imax> j jmax)
    AccE (i, j max ) = 00
    As another example, if the subsection has an unknown start node and an unknown end node, part 1002 can remain the same.
    In block 710, the computing device 112 determines a path through the accumulated error matrix. The path may give the smallest overall error in a correlation between your well logs for the subsection. For example, a starting point for the subsection may be known but an ending point for the subsection may be unknown. In such an example, the computing device 112 can determine the route at least in part by determining an end point for the subsection. For example, each point (eg, each coordinate (node b nodej)) in the accumulated error matrix may include the average of errors to arrive at that point. The computing device 112 can analyze the points along the highest line and the rightmost column in the accumulated error matrix to determine which of the points has the lowest average error. The computing device 112 can use this point as an end point. Now that the starting point and the end point are known, the computing device 112 can determine a path between the two points using a process called back tracking.
    Backtracking includes determining a path between a start point and an end point in the accumulated error matrix using the Dir matrix described above with reference to Figure 10. The Dir matrix may contain the direction to a previous point. The computing device 112 can backtrack the path between the start point and the end point by starting at the end point, finding the previous point and adding it to a list. The computing device 112 can then go back from the previous point, find yet another previous point and add this previous point to the list. The computing device 112 can repeat this process until
    2016-IPM-100868-U1-EN 15 that the route arrives at the starting point. An example of such a method is illustrated using the pseudo code;
    CurrentNodes (x, y) = (terminall, terminal])
    While 3> 2 {
    ifDir (x, y) == 1 addthefollowingnodetothepath (x - l, y - 2)
    CurrentNodes (x, y) = (x - l, y - 2) ifDir (x, y) == 2 addthefollowingnodetothepath (x - l, y - 1)
    CurrentNodes (x, y) - (x - l, y - 1) ifDir (x, y) 3 addthefollowingnodetothepath (x - 2, y - 1)
    CurrentNodes (x, y) ~ (x - 2, y - 1) ifDir (x, y) == 0
    Stop. You have reached one of the possible starting points for the journey.
    }}
    In block 712, the computing device 112 determines whether all of the subsections have been analyzed. If the computing device 112 determines that all of the subsections have not been analyzed, the method may revert to block 704 and choose another sub-section for analysis. If the computing device 112 determines that all of the subsections have been analyzed, the method can proceed to block 714.
    In block 714, the computing device 112 combines the paths (eg, determined in block 710) from all of the subsections, into a single combined path. The combined path can be a set of correlated nodes between the well logs and the well-log pair. In some examples, the computing device 112 can add together all the routes determined for all the subsections in a combined route. For example, FIG. 11A illustrates an example of sub-routes through each of the sub-error matrices of FIG. 9. FIG. 11B illustrates an example of a path completed through the error matrix 900 and formed by associating the sub -trajects through your error sub-matrices.
    The process of Figure 7 can be repeated for each daily well pair. For example, the computing device 112 can repeat the method of FIG. 7 to determine a combined path between each pair of well-logs identified in the block
    2016-IPM-100868-U1-FR 16
    602 of Figure 6, In some examples, after the process of Figure 7, the process of Figure 6 can continue to block 606.
    In block 602, the computing device 112 transforms the multiple well logs into a flat space based on the respective correlation between each well-log pair (e.g., using the combined path for each well-log pair determined in the block 714). The flat space can be a coordinate system in which all the nodes which correspond to the same stratum surface are positioned in the same two-dimensional plane (eg, have the same depth). In some examples, the computing device 112 may use the respective correlations between each well-log pair to form an equation system representing the offsets of the well logs in the flat space.
    For example, the computing device 112 can determine that the node / in the Well I is correlated to the node j in the Well J. The computing device 112 can transform the values for the node i and the node j into flat space, in which the two nodes will have the same depth value. In order to transform the values of node i and node j into flat space, an offset can be determined which is equal to the difference in depth values between te node / and node y. This offset can be represented by the following offset equation:
    Wellj) - shift (j, Wellj) = Zj - z ^ in which zj represents a value for the node j and z f represents a value for the node i. The computing device 112 can repeat this process for each correlation (nodej, nodej) in each well-journal pair to form a system of equations. The system of equations can form a matrix, which can be represented as:
    D s s "d s in which D s can be a hollow matrix operator and having negative ones which can be used to determine the difference between the shifts in the above equation; s can be a column having the offsets of each node of each well log in the flat space; and d s can be a column expressing the difference in depth for the correlated nodes (eg, (zj Zi)).
    In some examples, some of the rows in the above-mentioned matrix will correspond to the correlations resulting from manual corrections and other lines in the matrix will correspond to the automatically determined correlations. The rows in the matrix corresponding to manual corrections can
    2016-IPM-100868-U1-EN 17 can be weighted (eg multiplied by a weighting constant) to give them more weight than the automatically determined correlations.
    In some examples, there may be more pairs of correlated nodes than total nodes in all well logs (eg, since each individual node can be correlated with multiple other nodes). This can lead to overdetermination in the aforementioned matrix. Consequently, the offsets can be determined by minimizing the quadratic error of the matrix according to the following equation;
    min | D s s - d s | 2
    This can be called a least squares optimization. By performing Least Square Optimization, the error in a calculated stratum area can be propagated across all well logs. This can lead to a more precise approximation of the stratum surface than other approaches, which can propagate the error from one log well to another. For example, Figure 12A illustrates an area of the stratum (the dotted line) that has been determined using least squares optimization. FIG. 12A also illustrates the surface of the real stratum (the solid line). The area of the stratum determined using Least Square Optimization is close to the area of the actual stratum, and the error between the two surfaces is evenly distributed among wells 1 to 5. However, Figure 12B illustrates an area of the stratum (the dotted line) determined by the propagation of errors from one well log to another. An example of the propagation of errors from one well log to another may include the propagation of the error from a starting point 1202 from a surface of the stratum in well 1 to well 2, and well 2 to well 3 and well 3 to well 4, and so on. As shown in Figure 12B, the amount of errors continues to increase, resulting in failure to connect the end point 1204 of the stratum surface to the original start point 1202.
    After determining the offsets to the flat space, the computing device 112 can use the offsets to transform the well logs into flat space.
    In block 608, the computing device 112 transforms the nodes having the same depth value in the flat space into a depth domain to determine the updated versions of one or more surfaces of the stratum. For example, once all the well logs are transformed into flat spaces, the computing device 112 can identify all the nodes that have the same depth value in the flat space. The computing device 112 can then transform these
    2016-IPM-100868-U1-FR 18 nodes in a depth domain, in which they represent the updated versions of one or more surfaces of the stratum.
    In block 610, the computer device 112 displays the updated surface of the stratum. For example, the computing device 112 may display a GUI that includes some or all of the well logs and a line (eg, a solid or dotted line) indicating the updated area of the stratum. An example of such a GUI has been previously described with respect to FIG. 4.
    In some examples, some or all of the above features may be implemented using the computing device 112 illustrated in Figure 13. The computing device 112 may include a processing device 1304, a bus 1306, a memory device 1308, a user input device 1316, display device 1318, and communication interface 1320. In some examples, some or all of the components shown in Figure 13 may be integrated into a single structure, such as a housing unique. In other examples, some or all of your components shown in Figure 13 can be distributed (eg, in separate packages) and in electrical communication with each other.
    The processing device 1304 can execute one or more operations to automatically correlate the surfaces of the strata through the well logs. The processing device 1304 can execute instructions stored in the memory device 1308 to carry out the operations. The processing device 1304 may include a processing device or multiple processing devices. Non-limiting examples of the processing device 1304 may include a network of programmable field gates (FPGA), an application-specific integrated circuit (ASIC), a microprocessor, etc.
    The processing device 1304 can be coupled in communication to the memory device 1308 through the bus 1306. The non-volatile memory device 1308 can include any type of memory device which keeps the stored information when it is switched off. Non-limiting examples of the memory device 1308 include an electrically erasable programmable read-only memory (EEPROM), a flash memory, or any other type of non-volatile memory. In some examples, at least some of the memory device 1308 may include a medium from which processor 1304 can read instructions. A computer readable medium may include electronic, optical, mechanical or other types of storage devices which can provide the
    2016-IPM-100868-U1-EN 19 device 1304 for processing computer-readable instructions or other program codes. Non-limiting examples of computer readable media include (but are not limited to) magnetic disk (s), memory chip (s), read only memory (ROM), random access memory (RAM), ASIC, processing device configured, optical storage, or any other medium from which a computer processing device can read instructions. The instructions may include processing device-specific instructions generated by a compiler or interpreter from code written in any computer programming language, including, for example, C, C +++, C #, etc.
    In some examples, memory device 1308 can include well logs 1310. Well logs 1310 can be transmitted to computer device 112 from one or more well tools positioned in one or more wells.
    In some examples, the memory device 1308 can comprise a well-log correlation engine 1312. The well-log correlation engine 1312 can be a software application making it possible to identify the surfaces of the strata in the well logs 1310 and correlate strata surfaces among well logs 1310. More specifically, in some examples, the well-log correlation engine 1312 can correlate nodes in well logs 1310 to form multiple stratum-surface correlations. The log-well correlation engine 1312 can then receive input from the user indicating a correction to a given stratum-surface correlation of the multiple stratum-surface correlations. The log well correlation engine 1312 can update some or all of the remaining stratum-surface correlations based on user input.
    In some examples, the memory device 1308 can include a correlation database 1314. The correlation database 1314 can be a database with stratum-surface correlations. For example, the correlation database 1314 may include a node association in the well logs 1310 which belongs to a given stratum area.
    In some examples, the computing device 112 includes a user input device 1316. The user input device 1316 may represent one or more components used to enter data. Examples of 1316 user input devices may include a keyboard, mouse, touchpad, key or touchscreen, etc.
    In some examples, the computing device 112 includes a
    2016-IPM-100868-U1-EN 20 display device 1318, The display device 1318 can represent one or more components used to output data. Examples of the display device 1318 may include a liquid crystal display (ECL), a television, a computer monitor, a touch screen, etc. In some examples, the user input device 1316 and the display device 1318 may be a single device, such as a touch screen.
    In some examples, the computing device 112 includes a communication interface 1320. The communication interface 1320 can represent one or more components which facilitate a network connection or otherwise facilitate communication between electronic devices. Examples include, without limitation, surfil interfaces such as Ethernet, USB, IEEE 1394 and / or wired interfaces such as ΓΙΕΕΕ 802.11, Bluetooth, near field communication (NFC) interfaces, REID interfaces or radio interfaces to access cell phone networks (e.g. transceiver / antenna to access CDMA, GSM, UMTS, or other mobile communication networks).
    In some aspects, strata surfaces can be correlated through well logs according to one or more of the following examples;
    Example no. 1; One method includes receiving, by a processing device, a plurality of well logs. Each well log of the plurality of well logs may indicate subterranean strata penetrated by a respective wellbore associated with the well log. The method may include correlating, by the processing device, data points in the plurality of well data to form a plurality of stratum-surface correlations. Each stratum-surface correlation can be a respective association between the data points which correspond to a respective surface of a respective stratum of the underground strata. The method may include receiving, by the processing device, input from the user indicating a correction to a given stratum-surface correlation of the plurality of stratum-surface correlations. The method may include updating, by the processing device and based on user input, other stratum-surface correlations that are different from the stratum-surface correlation given in the plurality of stratum-surface correlations.
    Example no. 2: The process of Example no. 1 can include updating the other stratum-area correlations by combining all the well logs in the plurality of well logs into a plurality of well-log pairs.
    Each well-log pair in the plurality of well-log pairs may include two
    2016-IPM-100868-U1-FR 21 well logs among the plurality of well logs. A well-log pair can then be selected from the plurality of well-log pairs. A correlation between a first log well and a second log well in the log well pair can be determined by performing dynamic time alignment. The other stratum-surface correlations can be updated based at least in part on the correlation between the first log-well and the second log-well in the well-log pair.
    Example no. 3: The process of Example no. 2 may include determining the correlation between the first log well and the second log well in the log well pair by dividing the log well pair into a plurality of subsections based on user input. A plurality of paths through a plurality of accumulated error matrices associated with the plurality of sections can be generated. Each path of the plurality of paths may correspond to a respective accumulated error matrix and a respective subsection. The plurality of paths can be combined into a single path.
    Example no. 4; The process of Example no. 3 can comprise the generation of the plurality of paths through the plurality of accumulated error matrices, for each respective sub-section of the plurality of sub-sections, by generating a respective error sub-matrix corresponding to the sub- respective section based on user input. The respective error sub-matrix can overlap from a first point in an error matrix to a second point in the error matrix. At least one of the first point or the second point can be determined based on user input. A respective accumulated error matrix for the subsection can be generated based on the respective error sub-matrix. A respective path through the respective accumulated error matrix can be determined using the backtracking method.
    Example no. 5: The process of any of Examples no. 3 to 4 may include transforming the plurality of well logs into flat space based on the plurality of paths through the plurality of error matrices associated with the plurality of well-log pairs. The flat space can be a coordinate system in which the data points associated with the same stratum-surface are positioned in a two-dimensional plane.
    Example no. 6: The process of any of Examples no. 1 to 5 can include the display of a graphical user interface (GUI) visually representing an initial position of a stratum surface corresponding to the given stratum-surface correlation. User input can include dragging a
    2016-IPM-100868-U1-EN 22 component of the GUI from a first location indicating the initial position of the stratum surface to a second location indicating a corrected position for the stratum surface.
    Example no. 7: The process of any of Examples no. 1 to 6 can include updating a GUI to visually represent updated versions of other stratum-surface correlations.
    Example no. 8: A system may include a processing device and a memory device which includes instructions executable by the processing device. The instructions may cause the processing device to receive a plurality of well logs. Each well log of the plurality of well logs may indicate subterranean strata penetrated by a respective wellbore associated with the well log. The instructions may cause the processing device to correlate data points in the plurality of well logs to form a plurality of stratum-surface correlations. Each stratum-surface correlation can be a respective association between the data points which correspond to a respective surface of a respective stratum of the underground strata. The instructions may cause the processing device to receive input from the user indicating a correction to a given stratum-surface correlation of the plurality of stratum-surface correlations. The instructions may cause the processing device to update, based on user input, other stratum-surface correlations which are different from the stratum-surface correlation given in the plurality of stratum-surface correlations.
    Example no. 9: The system of Example no. 8 may include the fact that the memory device also includes instructions executable by the processing device to cause the processing device to update the other stratum-surface correlations by associating all the well logs in the plurality of well logs in one plurality of well-log pairs. Each well-log pair in the plurality of well-log pairs may include two of the plurality of well logs. A well-log pair can then be selected from the plurality of well-log pairs. A correlation between a first log well and a second log well in the log well pair can be determined by performing dynamic time alignment. The other stratum-surface correlations can be updated based at least in part on the correlation between the first log well and the second log-well in the log-well pair.
    Example no. 10: The system of Example no. 9 may understand that the memory device also includes instructions executable by the
    2016-IPM-100868-U1-EN 23 processing device to cause the processing device to determine the correlation between the first log well and the second log well in a log well pair by dividing the log well pair into a plurality of subsections based on user input. A plurality of paths through a plurality of accumulated error matrices associated with the plurality of sections can be generated. Each path of the plurality of paths may correspond to a respective accumulated error matrix and a respective subsection. The plurality of paths can be combined into a single path.
    Example no. 11; The system of Example no. 10 may include the fact that the memory device includes instructions executable by the processing device to cause the processing device to generate the plurality of paths through the plurality of accumulated error matrices, for each respective subsection of the plurality of subsections, generating a respective error sub-matrix corresponding to the respective subsection based on User Center. The respective error sub-matrix can overlap from a first point in an error matrix to a second point in the error matrix. At least one of the first point or the second point can be determined based on User Center. A respective accumulated error matrix for the subsection can be generated based on the respective error sub-matrix. A respective path through the respective accumulated error matrix can be determined using the backtracking method.
    Example no. 12: The system of any of Examples no. 10 to 11 may include the fact that the memory device also includes instructions executable by the processing device to cause the processing device to transform the plurality of well logs into a flat space based on the plurality of paths through the plurality error matrices associated with the plurality of well-log pairs. The flat space can be a coordinate system in which the data points associated with the same stratum-surface are positioned in a two-dimensional plane.
    Example no. 13: The system of any of Examples no. 8 to 12 may include the fact that the memory device also includes instructions executable by the processing device to cause the processing device to display a graphical user interface (GUI) visually representing an initial position of a stratum surface corresponding to the given stratum-surface correlation. User input may include dragging a GUI component from a first location indicating the initial position of the stratum surface to a second location indicating a corrected position for the stratum surface .
    2016-IPM-100868-U1-FR 24
    Example no. 14; The system of any of Examples no. 8 to 13 can understand that the memory device also includes instructions executable by the processing device to cause the processing device to update a GUI to visually represent updated versions of the other stratum-surface correlations.
    Example no. 15; A non-transient computer-readable medium may include instructions executable by the processing device. The instructions may cause the processing device to receive a plurality of well logs. Each well log of the plurality of well logs may indicate subterranean strata penetrated by a respective wellbore associated with the well log. The instructions may cause the processing device to correlate data points in the plurality of well logs to form a plurality of stratum-surface correlations. Each stratum-surface correlation can be a respective association between the data points which correspond to a respective surface of a respective stratum of the underground strata. The instructions may cause the processing device to receive input from the user indicating a correction to a given stratum-surface correlation of the plurality of stratum-surface correlations. The instructions may cause the processing device to update, based on user input, other stratum-surface correlations which are different from the stratum-surface correlation given in the plurality of stratum-surface correlations.
    Example no. 18: The non-transient computer-readable medium of Example no. 15 may include instructions executable by the processing device to cause the processing device to update the other stratum-area correlations by associating all the well logs in the plurality of well logs into a plurality of well-log pairs. Each well-log pair in the plurality of well-log pairs may include two of the plurality of well logs. A well-log pair can then be selected from the plurality of well-log pairs. A correlation between a first log well and a second log well in the log well pair can be determined by performing dynamic time alignment. The other stratum-surface correlations can be updated based at least in part on the correlation between the first log well and the second log-well in the log-well pair.
    Example no. 17: The non-transient computer-readable medium of Example no. 16 may include instructions executable by the processing device to cause the processing device to determine the correlation between the
    2016-IPM-100868-U1-EN 25 first log well and the second log well in a log well pair by dividing the log well pair into a plurality of subsections based on user input. A plurality of paths through a plurality of accumulated error matrices associated with the plurality of sections can be generated. Each path of the plurality of paths may correspond to a respective accumulated error matrix and a respective subsection. The plurality of paths can be combined into a single path.
    Example no. 18: The non-transient computer-readable medium of Example no. 17 may include instructions executable by the processing device to cause the processing device to generate the plurality of paths through the plurality of error matrices accumulated in, for each respective subsection of the plurality of subsections, generating a respective error sub-matrix corresponding to the respective sub-section based on user input. The respective error submaster can overlap from a first point in an error matrix to a second point in the error matrix. At least one of the first point or the second point can be determined based on user input. A respective accumulated error matrix for the subsection can be generated based on the respective error sub-matrix. A respective path through the respective accumulated error matrix can be determined using the backtracking method.
    Example no. 19: The non-transient computer-readable medium of any of Examples no. 17 to 18 may include instructions executable by the processing device to cause the processing device to transform the plurality of well logs into a flat space based on the plurality of paths through the plurality of error matrices associated with the plurality of well-log pairs. The flat space can be a coordinate system in which your data points associated with the same stratum-surface are positioned in a two-dimensional plane.
    Example no. 20: The non-transient computer-readable medium of any of Examples no. 15 to 19 may include instructions executable by the processing device to cause the processing device to display a graphical user interface (GUI) visually representing an initial position of a stratum surface corresponding to the given stratum-surface correlation. User input may include dragging a GUI component from a first location indicating the initial position of the stratum surface to a second location indicating a corrected position for the stratum surface .
    The foregoing description of certain examples, including the illustrated examples, has been presented for illustrative purposes only and is not
    2016-IPM-100868-U1-EN 26 envisaged that the description is exhaustive or that it limits the description of the precise forms described. Many modifications, adaptations and uses thereof will be apparent to a specialist in the field without departing from the scope of the disclosure.
    权利要求:
    Claims (15)
    [1" id="c-fr-0001]
    Claims
    1. Method for correlating the surfaces of strata through well logs comprising:
    receiving (504), by a processing device (1304), a plurality of well logs (208, 210, 212; 302, 304, 306), each well log of the plurality of well logs indicating underground strata (106a-c) penetrated by a respective drilling well (102a-h) associated with the well log;
    the correlation (508), by the processing device, of the data points in the plurality of well logs to form a plurality of stratum-surface correlations, each stratum-surface correlation being a respective association between the data points which correspond to a respective surface of a respective stratum of the underground strata;
    receiving, by the processing device, input from the user indicating a correction to a given stratum-surface correlation of the plurality of stratum-surface correlations; and updating (512), by the processing device and based on user input, other stratum-surface correlations that are different from the stratum-surface correlation given in the plurality of stratum-surface correlations.
    [2" id="c-fr-0002]
    2. Method according to claim 1, in which the updating (512) of the other stratum-surface correlations comprises:
    combining (602) all the well logs (208, 210, 212; 302, 304, 306) in the plurality of well logs into a plurality of daily well pairs, each well-log pair in the plurality of well pairs -log comprising two well logs among the plurality of well logs;
    selecting a well-log pair from the plurality of well-log pairs;
    determining (604) a correlation between a first log well and a second log well in the log well pair by performing dynamic time alignment; and updating the other stratum-area correlations based at least in part on the correlation between the first log well and the second log well in the log well pair.
    [3" id="c-fr-0003]
    3. Method according to claim 2, in which the determination (604) of the correlation between the first log well and the second log well in the log well pair comprises:
    dividing (702) the well-log pair into a plurality of subsections (802, 804, 806) based on user input; and generating a plurality of paths through a plurality of accumulated error matrices associated with the plurality of subsections, each path of the plurality of paths corresponding to a respective accumulated error matrix and a subsection respective; and combining (714) the plurality of paths into a single path.
    [4" id="c-fr-0004]
    4. The method as claimed in claim 3, in which the generation of the plurality of paths through the plurality of accumulated error matrices comprises, for each respective subsection (802, 804, 806) of the plurality of subsections:
    generating (706) a respective error sub-matrix corresponding to the respective sub-section based on user input, the respective error sub-matrix covering from a first point in a matrix d error (900) to a second point in the error matrix, wherein at least one of the first point or the second point is determined based on user input;
    generating (708) a respective accumulated error matrix for the respective sub-section based on the respective error sub-matrix; and determining (710) a respective path through the respective accumulated error matrix using a backtracking method.
    [5" id="c-fr-0005]
    The method of claim 3, further comprising transforming (602) the plurality of well logs (208, 210, 212; 302, 304, 306) into a flat space based on the plurality of paths through the plurality of accumulated error matrices associated with the plurality of sink-log pairs, in which the flat space is a coordinate system in which the data points associated with the same stratum-surface are positioned in a two-dimensional plane.
    [6" id="c-fr-0006]
    6. Method according to any one of claims 1 to 5, also comprising the display of a graphical user interface (GUI) visually representing an initial position of a stratum surface corresponding to the given stratum-surface correlation, in which user input includes dragging a GUI component from a first location indicating the initial position of the stratum surface to a second location indicating a corrected position for the stratum surface.
    [7" id="c-fr-0007]
    The method of claim 6, also comprising updating the GUI to visually represent updated versions of the other stratum-surface correlations.
    [8" id="c-fr-0008]
    8. Correlation system for strata surfaces through well logs (112) comprising;
    a processing device (1304); and a memory device (1308) which includes instructions executable by the processing device to cause the processing device to:
    receiving a plurality of well logs (1310), each well log of the plurality of well logs indicating subterranean strata penetrated by a respective wellbore associated with the well log;
    correlating data points in the plurality of well logs to form a plurality of stratum-surface correlations, each stratum-surface correlation being a respective association between the data points which correspond to a respective surface of a respective stratum of the underground strata ;
    receiving input from the user indicating a correction to a given stratum-surface correlation of the plurality of stratum-surface correlations; and updating, based on user input, other stratum-surface correlations that are different from the stratum-surface correlation given in the plurality of stratum-surface correlations.
    [9" id="c-fr-0009]
    The system (112) of claim 8, wherein the memory device (1308) also includes instructions executable by the processing device (1304) to bring the processing device to;
    update the other stratum-surface correlations by;
    combining all of the well logs (1310) in the plurality of well logs into a plurality of well-log pairs, each well-log pair of the plurality of well-log pairs comprising two of the well wells well logs;
    selecting a well-log pair from the plurality of well-log pairs;
    determining a correlation between a first log well and a second log well in the log well pair by performing dynamic time alignment; and updating the other stratum-area correlations based at least in part on the correlation between the first log well and the second log well in the well-log pair; and determining the correlation between the first log well and the second log well in the well-log pair by:
    dividing the sink-log pair into a plurality of subsections based on user input;
    generating a plurality of paths through a plurality of accumulated error matrices associated with the plurality of subsections, each path of the plurality of paths corresponding to a respective accumulated error matrix and a respective sub-section ; and combining the plurality of paths into a single path.
    [10" id="c-fr-0010]
    The system (112) of claim 9, wherein the memory device (1308) also includes instructions executable by the processing device (1304) to cause the processing device to generate the plurality of paths through the plurality of error matrices accumulated by, for each respective subsection of the plurality of subsections:
    generating a respective error sub-matrix corresponding to the respective sub-section based on user input, the respective error sub-matrix covering from a first point in a error to a second point in the error matrix, wherein at least one of the first point or the second point is determined based on user input;
    generating a respective accumulated error matrix for the respective sub-section based on the respective error sub-matrix; and determining a respective path through the respective accumulated error matrix using a backtracking method.
    [11" id="c-fr-0011]
    11. A non-transient computer readable medium which includes instructions executable by a processing device (1304) to bring the processing device to:
    receiving a plurality of well logs (1310), each well log of the plurality of well logs indicating subterranean strata penetrated by a respective wellbore associated with the well log;
    correlating data points in the plurality of well logs to form a plurality of stratum-surface correlations, each stratum-surface correlation being a respective association between the data points which correspond to a respective surface of a respective stratum of the underground strata;
    receiving input from the user indicating a correction to a given stratum-surface correlation of the plurality of stratum-surface correlations; and updating, based on user input, other stratum-surface correlations that are different from the stratum-surface correlation given in the plurality of stratum-surface correlations.
    [12" id="c-fr-0012]
    12. A non-transient computer-readable medium according to claim 11, also comprising instructions executable by the processing device (1304) to cause the processing device to update the other strataurface correlations by:
    combining all of the well logs (1310) in the plurality of well logs into a plurality of well-log pairs, each well-log pair of the plurality of well-log pairs comprising two of the well wells well logs;
    selecting a well-log pair from the plurality of well-log pairs;
    determining a correlation between a first log well and a second log well in the log well pair by performing dynamic time alignment; and updating the other stratum-area correlations based at least in part on the correlation between the first log well and the second log well in the log well pair.
    [13" id="c-fr-0013]
    13. Non-transient computer-readable medium according to claim
    12, also comprising instructions executable by the processing device (1304) to cause the processing device to determine the correlation between the first log well and the second log well in the well-log pair by:
    dividing the sink-log pair into a plurality of subsections based on user input; and generating a plurality of paths through a plurality of accumulated error matrices associated with the plurality of subsections, each path of the plurality of paths corresponding to a respective accumulated error matrix and a subsection respective; and combining the plurality of paths into a single path.
    [14" id="c-fr-0014]
    14. Non-transient computer-readable medium according to claim
    13, also comprising instructions executable by the processing device (1304) to cause the processing device to generate the plurality of paths through the plurality of error matrices accumulated by, for each respective subsection of the plurality of subs -sections:
    generating a respective error sub-matrix corresponding to the respective sub-section based on user input, the respective error sub-matrix covering from a first point in a error to a second point in the error matrix, wherein at least one of the first point or the second point is determined based on user input;
    generating a respective accumulated error matrix for the respective sub-section based on the respective error sub-matrix; and determining a respective path through the respective accumulated error matrix using a backtracking method.
    [15" id="c-fr-0015]
    15. The non-transient computer-readable medium of claim 13, also comprising instructions executable by the processing device (1304) to cause the processing device to transform the plurality of well logs (1310) into a flat space based on the plurality of paths through the plurality of accumulated error matrices associated with the plurality of sink-log pairs, in which the flat space is a coordinate system in which the data points associated with the same stratum-surface are positioned in a two-dimensional plan.
    2016-IPM-100868-U1-FR 1/14 ne. 1
    2016-IPM-100868-U1-FR 2/14
    2016-IPM-100868-U1-FR 3/14
    SilDd 31 SNVQ 31NVSSI080 HrBQNOJGÜd fjjlflli a ·· cn o
    LL.
    2016-IPM-100868-U1-FR 4/14
    Slind 31 SNVQ 31NVSSIO8O HfOQNOdObd £ 2
    2016-IPM-100868-U1-FR 5/14
    DETERMINE WELLS OF INTEREST
    RECEIVE WELL LOGS ASSOCIATED WITH WELLS OF INTEREST
    PRETREAT WELL LOGS
    IDENTIFY AND CORRELATE THE TRACKS AMONG PRETREATED WELL LOGS
    NO ^ -RECEIVE UNF CORRECTION MANDE i 1 E ON A SURFACE OF 5TRATES DATA DESIRED
    DETERMINING AN UPDATED VERSION OF ONE OR MORE OTHER STRATE SURFACES BASED ON MANUAL CORRECTION
    DISPLAY A GRAPHICAL USER INTERFACE VISUALLY REPRESENTING THE
    STRATE SURFACES
    -Cl * u 1
    类似技术:
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    同族专利:
    公开号 | 公开日
    GB201910860D0|2019-09-11|
    NO20190935A1|2019-07-30|
    WO2018164680A1|2018-09-13|
    US20210405238A1|2021-12-30|
    GB2573464B|2022-02-16|
    GB2573464A|2019-11-06|
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    法律状态:
    2019-01-22| PLFP| Fee payment|Year of fee payment: 2 |
    2020-05-01| PLSC| Publication of the preliminary search report|Effective date: 20200501 |
    2020-10-16| ST| Notification of lapse|Effective date: 20200910 |
    优先权:
    申请号 | 申请日 | 专利标题
    IBWOUS2017021361|2017-03-08|
    PCT/US2017/021361|WO2018164680A1|2017-03-08|2017-03-08|Correlating strata surfaces across well logs|
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