WO2017135972A1 - Système et procédé d'analyse des données de diagraphie de sondage - Google Patents

Système et procédé d'analyse des données de diagraphie de sondage Download PDF

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Publication number
WO2017135972A1
WO2017135972A1 PCT/US2016/016889 US2016016889W WO2017135972A1 WO 2017135972 A1 WO2017135972 A1 WO 2017135972A1 US 2016016889 W US2016016889 W US 2016016889W WO 2017135972 A1 WO2017135972 A1 WO 2017135972A1
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WIPO (PCT)
Prior art keywords
well
formation
depth
characterizations
edges
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PCT/US2016/016889
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English (en)
Inventor
Iwao TANUMA
Ravigopal Vennelakanti
Anshuman SAHU
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Hitachi, Ltd.
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Application filed by Hitachi, Ltd. filed Critical Hitachi, Ltd.
Priority to PCT/US2016/016889 priority Critical patent/WO2017135972A1/fr
Publication of WO2017135972A1 publication Critical patent/WO2017135972A1/fr

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Classifications

    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V11/00—Prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00

Definitions

  • the present disclosure is generally directed to oil and gas systems, and more specifically, to systems and methods for well log data analysis.
  • oil and gas rigs utilize computerized systems to assist the operators of the rigs throughout the different phases of the oil or gas rigs (e.g., exploration, drilling, production, completions).
  • Such computer systems are deployed for the development of energy sources such as shale gas, oil sands, and deep water resources.
  • energy sources such as shale gas, oil sands, and deep water resources.
  • attention has shifted to the development of shale gas for supplying future energy needs.
  • Related art advances in horizontal directional drilling and hydraulic fracturing technologies have unlocked the potential for recovering natural gas from shale to become a viable energy source.
  • NPT Non-Productive Time
  • Example implementations are directed to methods and systems for calculating and visualizing information of formation and drilling operation collected around wellbores.
  • aspects of the present disclosure includes a management server configured to manage a plurality of wells, the management server involving a memory, configured to store sensor data of the plurality of wells; and a processor, configured to determine, for a first well of the plurality of wells, a second well from the plurality of wells that neighbors the first well within a distance threshold; determine, from the sensor data, first depth ranges associated with first formation characterizations of the first well, and second depth ranges associated with second formation characterizations of the second well; match the first depth ranges of the first well to the second depth ranges of the second well based on matching the first formation characterizations of the first well to the second formation characterizations of the second well; determine areas between the first well and the second well corresponding to the first depth ranges and the second depth ranges; and determine formation characterizations for each of the areas based on the matching of the first formation characterizations to the second formation characterizations.
  • aspects of the present disclosure further includes a method for managing a plurality of wells, which can involve managing sensor data of the plurality of wells; determining, for a first well of the plurality of wells, a second well from the plurality of wells that neighbors the first well within a distance threshold; determining, from the sensor data, first depth ranges associated with first formation characterizations of the first well, and second depth ranges associated with second formation characterizations of the second well; matching the first depth ranges of the first well to the second depth ranges of the second well based on matching the first formation characterizations of the first well to the second formation characterizations of the second well; determining areas between the first well and the second well corresponding to the first depth ranges and the second depth ranges; and determining formation characterizations for each of the areas based on the matching of the first formation characterizations to the second formation characterizations.
  • aspects of the present disclosure further includes a computer program for managing a plurality of wells, which can involve managing sensor data of the plurality of wells; determining, for a first well of the plurality of wells, a second well from the plurality of wells that neighbors the first well within a distance threshold; determining, from the sensor data, first depth ranges associated with first formation characterizations of the first well, and second depth ranges associated with second formation characterizations of the second well; matching the first depth ranges of the first well to the second depth ranges of the second well based on matching the first formation characterizations of the first well to the second formation characterizations of the second well; determining areas between the first well and the second well corresponding to the first depth ranges and the second depth ranges; and determining formation characterizations for each of the areas based on the matching of the first formation characterizations to the second formation characterizations.
  • the computer program may be stored on a non-transitory computer readable medium and executed by one or more processors or as implemented on
  • aspects of the present disclosure further includes an apparatus for managing a plurality of wells, which can involve means for managing sensor data of the plurality of wells; means for determining, for a first well of the plurality of wells, a second well from the plurality of wells that neighbors the first well within a distance threshold; means for determining, from the sensor data, first depth ranges associated with first formation characterizations of the first well, and second depth ranges associated with second formation characterizations of the second well; means for matching the first depth ranges of the first well to the second depth ranges of the second well based on matching the first formation characterizations of the first well to the second formation characterizations of the second well; means for determining areas between the first well and the second well corresponding to the first depth ranges and the second depth ranges; and means for determining formation characterizations for each of the areas based on the matching of the first formation characterizations to the second formation characterizations.
  • aspects of the present disclosure can further include a computer program, configured to manage a plurality of wells.
  • the computer program can involve instructions that include storing sensor data of the plurality of wells; determining, for a first well of the plurality of wells, a second well from the plurality of wells that neighbors the first well within a distance threshold; and determining first depths of the first well corresponding to second depths of the second well based on characteristics sensor data for the first well and the second well matching within a confidence level.
  • the computer program may be stored on a non-transitory computer readable medium and executed by one or more processors or as implemented on a management server.
  • FIG. 1(a) illustrates a system involving a plurality of rig systems and a management server, in accordance with an example implementation.
  • FIG. 1(b) illustrates an example timeline for a rig system, in accordance with an example implementation.
  • FIG. 2 illustrates an example rig in accordance with an example implementation.
  • FIG. 3 illustrates an example configuration of a rig system, in accordance with an example implementation.
  • FIG. 4 illustrates a configuration of a management server, in accordance with an example implementation.
  • FIG. 5 illustrates a system view, in accordance with an example implementation.
  • FIGS. 6(a) and 6(b) illustrate examples of results, in accordance with an example implementation.
  • FIG. 7 illustrates an example flow diagram for the system, in accordance with an example implementation.
  • FIGS. 8(a) and 8(b) illustrate example results for the matching calculation, in accordance with an example implementation.
  • FIGS. 9(a) and 9(b) illustrate another example of matching calculation results in accordance with an example implementation.
  • FIG. 10 illustrates a flow diagram in accordance with an example implementation.
  • FIGS. 11(a) and 11(b) illustrate example management information in accordance with an example implementation.
  • FIG. 12 illustrates an example matching in accordance with an example implementation.
  • FIG. 13 illustrates an example flow diagram for matching in accordance with an example implementation.
  • FIG. 14 illustrates an example flow diagram for calculating path and cost between a pair of points for wells, in accordance with an example implementation.
  • FIG. 15 illustrates an example visualization for confidence values for each depth, in accordance with an example implementation.
  • FIG. 16 illustrates an example interface upon which example implementations can be applied.
  • FIG. 17 illustrates an example of results from formation characterization selection, in accordance with an example implementation.
  • FIG. 1(a) illustrates a system involving a plurality of rig systems and a management server, in accordance with an example implementation.
  • One or more rig systems 101-1, 101-2, 101-3, 101-4, and 101-5 can each involve a corresponding rig 200- 1, 200-2, 200-3, 200-4, 200-5 as illustrated in FIG. 2 along with a corresponding rig node 300-1, 300-2, 300-3, 300-4, and 300-5 as illustrated in FIG. 3.
  • Each of the rig systems 101-1, 101-2, 101-3, 101-4, and 101-5 is connected to a network 100 which is connected to a management server 102.
  • the management server 102 manages a database 103, which contains data aggregated from the rig systems in the network 100.
  • the data from the rig systems 101-1, 101-2, 101-3, 101-4, and 101-5 can be aggregated to a central repository or central database such as public databases that aggregate data from rigs or rig systems, such as for government compliance purposes, and the management server 102 can access or retrieve the data from the central repository or central database.
  • a central repository or central database such as public databases that aggregate data from rigs or rig systems, such as for government compliance purposes, and the management server 102 can access or retrieve the data from the central repository or central database.
  • FIG. 1(b) illustrates an example timeline for a rig system, in accordance with an example implementation.
  • the timeline for the rig system 101 may include multiple phases of rig operation. These phases can include (but are not limited to) an exploration phase, a drilling phase, a completions phase, a production phase, a processing phase and a pipeline phase.
  • phases can include (but are not limited to) an exploration phase, a drilling phase, a completions phase, a production phase, a processing phase and a pipeline phase.
  • the term “process” may also be used interchangeably with the term "phase”.
  • Example implementations may involve data or attributes associated with one or more of the phases of the timeline, depending on the desired implementation.
  • the well is initially drilled to determine whether reservoirs with oil or gas are present and the initial construction of the rig.
  • the rig node may be configured to assist the user in determining how to configure the rig and the parameters for the drilling during the exploration phase.
  • the drilling phase follows the exploration phase as determined in the exploration phase, e.g., if promising amounts of oil and gas are confirmed from the exploration phase.
  • the size and characteristics of the discovery are determined and technical information is utilized to allow for more optimal methods for recovery of the oil and gas.
  • An appraisal drilling can be performed and a rig is established.
  • the rig node may be configured to assist the user in determining appropriate parameters for the drilling and assist in the management and obtaining of desired characteristics for the rig.
  • the completions phase is directed to the determination as to whether the well should be completed as a well, or whether it should be abandoned as a dry hole.
  • the completion phase transforms the drilled well into a producing well.
  • the casing of the rig may be constructed, along with the perforations.
  • Various aspects of the construction of the rig such as cementing, gravel packing and production tree installation may be employed.
  • Sensors may be employed to determine various parameters for facilitating the completion of the rig, such as rate of flow, flow pressure and gas to oil ratio measurements, but not limited thereto.
  • the production phase follows the completions phase and is directed to the facilitation of production of oil or gas.
  • the production phase includes the operation of wells and compressor stations or pump stations, waste management, and maintenance and replacement of facility components. Sensors may be utilized to observe the above operations, as well as determining environmental impacts from parameters such as sludge waste accumulation, noise, and so on.
  • Example implementations described herein may provide feedback to rig system operators to maximize the production of the rig based on the use of model signatures.
  • FIG. 2 illustrates an example rig 200 in accordance with an example implementation.
  • the example implementation depicted in FIG. 2 is directed to a shale gas rig.
  • the well 201 may include one or more gas lift valves 201-1 which are configured to control hydrostatic pressure of the tubing 201-2.
  • Tubing 201-2 is configured to extract gas from the well 201.
  • the well 201 may include a case 201-3 which can involve a pipe constructed within the borehole of the well.
  • One or more packers 201-4 can be employed to isolate sections of the well 201. Perforations 201- 5 within the casing 201-3 allow for a connection between the shale gas reservoir to the tubing 201-2.
  • the rig 200 may include multiple sub-systems directed to injection of material into the well 201 and to the production of material from the well 201.
  • a compressor system 202 that includes one or more compressors that are configured to inject material into the well such as air or water.
  • a gas header system 202 may involve a gas header 202-1 and a series of valves to control the injection flow of the compressor system 202.
  • a choke system 203 may include a controller or casing choke valve which is configured to reduce the flow of material into the well 201.
  • a wing and master valve system 204 which contains one or more wing valves configured to control the flow of production of the well 201.
  • a flowline choke system 205 may include a flowline choke to control flowline pressure from the well 201.
  • a production header system 206 may employ a production header 206-1 and one or more valves to control the flow from the well 201, and to send produced fluids from the well 201 to either testing or production vessels.
  • a separator system 207 may include one or more separators configured to separate material such as sand or silt from the material extracted from the well 201.
  • various sensors may be applied throughout the rig to measure various data or attributes for a rig node, which are described in further detail below.
  • the sensors are identified by an "S" in an octagon in FIG. 2. These sensors provide feedback to the rig node which can interact with the system as illustrated in FIGS. 1(a) and 1(b), and can be fed to the management server 102 for database storage 103, and/or supplied to a central repository or database such as a public database, which can then be harvested by management server 102.
  • FIG. 3 illustrates an example configuration of a rig system 101, in accordance with an example implementation.
  • the rig system 101 includes a rig 200 as illustrated in FIG. 2 which contains a plurality of sensors 210.
  • the rig system 101 includes a rig node 300 which may be in the form of a server or other computer device and can contain a processor 301, a memory 302, a storage 303, a data interface (I/F) 304 and a network I/F 305.
  • the data I/F 304 interacts with the one or more sensors 210 of the rig 200 and store raw data in the storage 303, which can be sent to a management server for processing as described in FIG. 4, or to a central repository or central database.
  • the network I/F 305 provides an interface to connect to the network 100.
  • FIG. 4 illustrates a configuration of a management server 102, in accordance with an example implementation.
  • Management server 102 may involve a processor 401, a memory 402, a storage I/F 404 and a network I/F 405.
  • the processor 401 is configured to execute one or more programs in the memory 402, to process data and for calculating composite similarity scores.
  • the storage I F 404 is the interface to facilitate connections between the management server 102 and the database 103.
  • the network I/F 405 facilitates interactions between the management server 102 and the plurality of rig systems. Data is aggregated to the management server 102 by the network I/F 405 and is then subsequently stored in the database, for example, for future analytics. Alternatively, the plurality of rig systems may send the data to a central database or repository, which is then processed by the management server 102.
  • the management server 102 may be configured to manage the plurality of wells as illustrated of FIG. 1(a).
  • Memory 402 may be configured to store sensor data and other information as illustrated in FIGS. 5, 11(a) and 11(b).
  • Processor 401 may be configured to invoke the algorithms as illustrated in FIG. 5, and process sensor data in accordance of the flow diagrams of FIGS. 12-14.
  • Processor 401 may be configured to determine, for a first well of the plurality of wells, a second well from the plurality of wells that neighbors the first well within a distance threshold, as illustrated in the flow diagram of FIG. 7.
  • Processor 401 may be further configured to determine, from the sensor data, first depth ranges associated with first formation characterizations of the first well, and second depth ranges associated with second formation characterizations of the second well, and match the first depth ranges of the first well to the second depth ranges of the second well based on matching the first formation characterizations of the first well to the second formation characterizations of the second well as illustrated in FIG. 9.
  • Processor 401 may also be configured to determine areas between the first well and the second well corresponding to the first depth ranges and the second depth ranges; and determine formation characterizations for each of the areas based on the matching of the first formation characterizations to the second formation characterizations as illustrated in FIGS. 8(a) and 8(b).
  • Processor 401 is configured to determine, from the sensor data, the first depth ranges associated with the first formation characterizations of the first well, and the second depth ranges associated with the second formation characterizations of the second well. For the sensor data corresponding to depth values of the first well and the second well, processor 401 can be configured to determine formation characterizations from the sensor data for the depth values that meet a confidence threshold and start depths of the formation characterizations; and for changes occurring in the formation characterizations from one formation characterization to another formation characterization, setting an end depth for each of the depth values corresponding to the changes to form the first depth ranges and second depth ranges as illustrated in FIG. 11(b) and FIG. 12.
  • Processor 401 is configured to match the first depth ranges of the first well to the second depth ranges of the second well based on matching the first formation characterizations of the first well to the second formation characterizations of the second well.
  • Processor 401 can be configured to match the first formation characterizations with corresponding equivalent formation characterizations of the second formation characterizations, match first edges of the first depth ranges to second edges of the second depth ranges based on the matching of the first formation characterizations with the corresponding equivalent formation characterizations, the first edges comprising start depths and end depths of the first depth ranges, the second edges comprising start depths and end depths of the second depth ranges, the matching the first edges to second edges conducted such that none of the second edges are matched with multiple edges of the first edges, and for the first edges arranged in order by depth, the matched second edges are also thereby ordered by depth as described in FIG.
  • Processor 401 may also be configured to determine confidence values for a plurality of formation characterizations for each of the depth values; and select each of the formation characterizations for each of the depth values from the plurality of formation characterizations based on the confidence values as illustrated in FIG. 16.
  • Processor 401 is further configured to determine areas between the first well and the second well corresponding to the first depth ranges and the second depth ranges by forming the areas between the first well and the second well from matched pairs of edges between the first well and the second well as illustrated in FIG. 8(a) and 8(b).
  • FIG. 5 illustrates a system view, in accordance with an example implementation.
  • the system can include functions, multiple algorithms, distance search, matching/measurement calculation and visualization part, and each part is connected to databases storing well coordinates, sensor data, Segment/change point/patterns and correspondence information.
  • Well coordinate information 500 includes the table which stores attributes, the corresponding Well ID (e.g. as a key value) and coordinates.
  • Well ID is the value which is uniquely determined by the well or the wellbore in multiple wells. Coordinates are representative of the coordinates corresponding to the well (for example wellhead longitude and latitude).
  • Sensor data information 501 includes sensor data series corresponding to the depth from wellbore, gamma ray, porosity, resistivity, caliper, and so on. All of the information is collected from the well corresponding to the Well ID.
  • Segment/change point/pattern information 502 includes results of the part of multiple algorithms, the segment, the change point, and pattern information corresponding to the sensor data.
  • Nearby wells information 503 includes the output of part of distance search, an attribute well ID as a key and the set of list of well ID linked to the key value.
  • Correspondence information 504 includes the results of the matching relationship calculations between the records in segment/change point/patterns information.
  • the multiple algorithms 505 can include, but is not limited to, multiple segmentation (e.g. Hidden Markov Model), anomaly/change point detection (e.g. Short Term Averaging / Long Term Averaging or STA/LTA), and pre-trained pattern classification (e.g. Neural Network) algorithms.
  • Each algorithm can be directed to at least one data series corresponding to the wellbore depth or coordinates from sensor data information 501.
  • the system receives a series of segment indexes (e.g. from segmentation algorithms), flags for the detected pattern identification (e.g. from anomaly/change point detection algorithms) and labels (e.g. from pre-trained pattern classification algorithms) corresponding to the wellbore depth or coordinates as output.
  • the distance search processes an existing well ID as an input (e.g. a named target), then refers to the well coordinate information 500 and calculates the distance (e.g. Euclidian distance) between the target and other existing wells.
  • Distance search function 506 then stores the well ID of the target and list of the wells which has a smaller distance than the predefined threshold to nearby wells information 503.
  • the pair of IDs is processed as input and refers to sensor data information 501 and segment/change point/patterns information 502 applying each of the well IDs as a key.
  • the matching algorithms are executed with sensor series data (e.g. Dynamic Time Warping between sensor data of pair of wells) or rule-based matching (e.g. matching start point and end point between labels of pair of wells), depending on the desired implementation.
  • the system visualizes at least one of the attributes from sensor data information 501, segment/change point/patterns information 502 and also correspondence information 504, including filtering function with predefined rules.
  • the desired database can be selected, as well as the desired attributes with the desired filtering.
  • FIGS. 6(a) and 6(b) illustrate examples of results, in accordance with an example implementation.
  • the pre-trained classifier 601 and segmentation 602 algorithms are configured to calculate labels or segments individually. Confidence values also can be calculated corresponding to each point of data series. The values indicate the confidence of each of the labels or segments. For example, in generative models (like Hidden Markov Models or HMM), the likelihood for each data point can be interpreted as confidence values.
  • HMM Hidden Markov Models
  • FIG. 6(a) illustrates an example implementation of a pre-trained classifier 601 with a classification example with a confidence value.
  • gamma ray values for a well are used as input across various depth levels.
  • the rock property of a well for a given depth level can be determined from use of the algorithm.
  • confidence values can also be provided for the classification of the depth level.
  • FIG. 6(b) illustrates an example implementation of a segmentation 602 algorithm showing a segmentation example with a confidence value.
  • gamma ray values for a well are used as input across various depth levels.
  • the output 602-2 based on execution of the segmentation 602 algorithm, the segment characteristics of the well are provided across the depth of the well.
  • FIG. 7 illustrates an example flow diagram for the system, in accordance with an example implementation.
  • the flow starts at 701 by detecting the new record in well coordinate DB or sensor data DB, batch or user execution can be a trigger for starting the flow.
  • the system stores the well ID of new records in well coordinate information 500 or sensor data information 501 or input in batch or user execution.
  • sensor data series corresponding to depth from wellbore, gamma ray, porosity, resistivity, caliper, and so on can be given as input from the sensor data DB derive from well ID stored in the last step.
  • Each algorithm is executed with at least one data series as an input. If a plurality of data series' are utilized as input, the input can be processed as a multi-dimensional vector series. For example, assume that there is an algorithm to process caliper as input, and an algorithm to detect anomalies with STA/LTA.
  • Another algorithm can be configured to process all of sensor data, and apply the pre-trained classifier and give the labels related to the formation (e.g. sandstone, shale, and so on) for each depth. Then, the results are stored in segment/change point/pattern DB.
  • example implementations search for existing wells around the target well (i.e. well which has the ID stored at 701). For each wells stored in well coordinate DB 500, the example implementations calculate the distance from the target well, and capture the set of neighbor well IDs from the target well within a predefined or desired threshold of distance. The pair of the target well ID and set of neighbor well IDs are then stored in nearby well DB 503. At 704, example implementations load the pair of the target well ID and set of neighbor well IDs stored. A matching calculation is executed for all of the wells stored in the set of well IDs with target well ID.
  • FIGS. 8(a) and 8(b) illustrate example results for the matching calculation, in accordance with an example implementation.
  • the example implementations read the labels of the pair of wells from segment/change point/pattern DB 502, compares both of the wells, and matches the start and the end points of each labels to produce the results as illustrated in FIG. 8(a) and 8(b).
  • Each of the labels indicates formation characterizations which can involve rock compositions.
  • the rock compositions can include shale, salt, anhydride, and so on, that occur between managed rig systems.
  • FIGS. 9(a) and 9(b) illustrate another example of matching calculation results in accordance with an example implementation.
  • sensor data is read for the pair of wells from the sensor data DB 501, compares the data, and matches the data between each depth with a matching algorithm such as dynamic time warping (DTW).
  • DTW dynamic time warping
  • the results are stored in correspondence DB 504.
  • the result of dynamic programming (DP) matching gives the depth to depth matching for all depth of two well.
  • the start point, end point and the point detected as a change in the segment, description or anomaly is presented.
  • Visualization functions can be executed as desired, and can provide visualizations for the result of multiple algorithms and the interpretation functions, such as the labeling of rock property and drilling operational anomaly corresponding to wellbore depth.
  • FIG. 10 illustrates a flow diagram in accordance with an example implementation.
  • pairs of well IDs are processed as input, wherein at 1001, the flow extracts a series of labels for both wells in a processed pair of wells.
  • the flow proceeds to determine an optimal set for label matching.
  • the flow stores the label matching results.
  • constraints can be applied.
  • example implementations for matching can involve finding the set of the edges which satisfies the above constraints (e.g. finite combination optimization problem). If there are a large amount of candidates of sets of edges, pickup with pre-defined criteria for example, the set of edges which has most nodes and minimizing sum of deference of depth for each edges.
  • Each edge represents one of the managed rig systems as illustrated in FIGS. 2 and 3.
  • FIGS. 11(a) and 11(b) illustrate example management information in accordance with an example implementation.
  • FIG. 11(a) illustrates an example of sensor data that can be provided in sensor data 501. Such data can include gamma ray, resistivity and so on.
  • FIG. 11(b) illustrates an example of a description of the formation of a well, which can be provided in segment/change data/pattern data 502.
  • the depths of the well are correlated with the rock formation found within the indicated depth ranges of the well, and derived from FIG. 11(a).
  • FIG. 1 1(b) provides a comparison between the depth range and the formation characterization found for a managed rig system.
  • FIG. 12 illustrates an example matching in accordance with an example implementation.
  • formation data as illustrated from FIG. 11(b) for one well is matched with the formation data for another well.
  • node set Nl the labels and depths are extracted at 1001.
  • the formation data for the well corresponding to node set Nl is matched with formation data for another well corresponding to node set N2.
  • the execution of the flow of 1002 is based on the constraints as described in FIG. 10.
  • the avoidance of node intersection is enforced, so that nodes are not intersected between the two wells.
  • FIG. 13 illustrates an example flow diagram for matching in accordance with an example implementation.
  • the confidence value vector series for the well pair X and Y is used as iMpel ⁇ , 3 ⁇ 4 , ⁇ ⁇ ⁇ , 3 ⁇ 4 ) Y - (y l , y 2 , ⁇ ⁇ ⁇ , y m )
  • cost constraints are set, as well as path constraints between the data sets for well X and well Y.
  • a loop is initiated to loop i for each data point in data set for well X at 1302. If the data points in the set X are not all processed (true), a loop is initiated for each data point in the data set for well Y at 1303.
  • a check is determined as to whether all data points for well Y has been processed. If not (true), then the flow proceeds to 1305 wherein the cost and the path between the data point of well X and the data point of well Y are calculated according to the desired implementation. The next data point in the data set for well Y is then selected at 1307 and the loop returns to 1304.
  • the flow proceeds to 1306 to select the next data point for the data set for well X and the flow proceeds to 1302.
  • the path is extracted between data points of the data set for well X and the data points of the data set for well Y at 1308, and the flow ends at 1309.
  • FIG. 14 illustrates an example flow diagram for calculating path and cost between a pair of points from well X and well Y in accordance with an example implementation.
  • a determination is made as to whether both data points from well X and well Y have the desired classification (e.g., both points correspond to classification of 50% sand with 50% shale, or 100% limestone, or so on). If so (yes), then the flow proceeds to 1402 to calculate the distance between the two points as being a straight line, and the path to be a straight line, whereupon the flow proceeds to 1408.
  • the desired classification e.g., both points correspond to classification of 50% sand with 50% shale, or 100% limestone, or so on.
  • the flow proceeds to 1403 to determine if the data point from well X contains the desired classification. If so (true), then the flow proceeds to 1404 wherein the cost is determined to be the cost between the data point from well X and the previous data point of well Y plus the distance between the data points. The path is constructed as the path from the data point from well X to the previous data point of well Y.
  • the flow proceeds to 1405 to determine if the data point from well Y contains the desired classification. If so (true), then the flow proceeds to 1406 wherein the cost is determined to be the cost between the data point from well Y and the previous data point of well X plus the distance between the data points. The path is constructed as the path from the previous data point from well X to the data point of well Y.
  • FIG. 15 illustrates an example visualization for confidence values for each depth, in accordance with an example implementation.
  • the labels of a first well are compared to the labels of another well as illustrated in FIG. 9(b).
  • the distance between each corresponding label vector is indicated at 1500.
  • the path that minimizes the distance between the corresponding label vectors is provided at 1501. ;
  • the confidence value fqr the n-th description as illustrated at 1502 is of the form
  • FIG. 16 illustrates an example interface upon which example implementations can be applied.
  • a click is made at mouse cursor 1601, which provides two windows of information 1602 and 1603.
  • information for the click point is revealed, which in the example of FIG. 16 is the weighted mean of the compositions of the different types of sediments at the selected depth.
  • the weighted mean is provided as an estimated value.
  • a color map visualization can also be provided.
  • the two closest paired points between welll and well2 is revealed, along with the sediment composition between the two wells.
  • Other attributes may be utilized instead, depending on the desired
  • FIG. 17 illustrates an example of results from formation characterization selection, in accordance with an example implementation.
  • the characterization may have a higher confidence value than the expected formation characterization.
  • the matching algorithm as illustrated in FIGS. 14 and 16 is utilized to conduct matching, wherein the expected formation characterization for the depth value of well 2 is "Stone 2", although the formation characterization "Stone 1" has a higher confidence value.
  • Stone 2 is thereby selected to meet the constraints as illustrated in FIGS. 14 and 16. The selection can be provided in a visualization which can also indicate that another formation characterization has a higher confidence value.
  • Example implementations may also relate to an apparatus for performing the operations herein.
  • This apparatus may be specially constructed for the required purposes, or it may include one or more general-purpose computers selectively activated or reconfigured by one or more computer programs.
  • Such computer programs may be stored in a computer-readable medium, such as a non-transitory medium or a storage medium, or a computer-readable signal medium.
  • Non-transitory media or non-transitory computer- readable media can be tangible media such as, but are not limited to, optical disks, magnetic disks, read-only memories, random access memories, solid state devices and drives, or any other types of tangible media suitable for storing electronic information.
  • a computer readable signal medium may any transitory medium, such as carrier waves.
  • the algorithms and displays presented herein are not inherently related to any particular computer or other apparatus.
  • Computer programs can involve pure software implementations that involve instructions that perform the operations of the desired implementation.
  • the operations described above can be performed by hardware, software, or some combination of software and hardware.
  • Various aspects of the example implementations may be implemented using circuits and logic devices (hardware), while other aspects may be implemented using instructions stored on a machine-readable medium (software), which if executed by a processor, would cause the processor to perform a method to carry out implementations of the present application.
  • some example implementations of the present application may be performed solely in hardware, whereas other example implementations may be performed solely in software.
  • the various functions described can be performed in a single unit, or can be spread across a number of components in any number of ways.
  • the methods may be executed by a processor, such as a general purpose computer, based on instructions stored on a computer-readable medium. If desired, the instructions can be stored on the medium in a compressed and/or encrypted format.

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  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Geology (AREA)
  • Mining & Mineral Resources (AREA)
  • Physics & Mathematics (AREA)
  • Environmental & Geological Engineering (AREA)
  • Fluid Mechanics (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Geochemistry & Mineralogy (AREA)
  • Testing Or Calibration Of Command Recording Devices (AREA)

Abstract

La présente invention concerne des systèmes et des procédés pour identifier de multiples facteurs incluant des informations de formation et aussi une opération de forage de puits à travers de multiples algorithmes. Des exemples de modes de réalisation peuvent faire appel à la comparaison des puits avec des puits voisins, et l'application d'une règle de filtrage prédéfinie d'interprétation des résultats de chaque algorithme ou des données de capteur, et la production d'un aperçu topographique des puits gérés par les exemples de mode de réalisation.
PCT/US2016/016889 2016-02-05 2016-02-05 Système et procédé d'analyse des données de diagraphie de sondage WO2017135972A1 (fr)

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US11481413B2 (en) 2020-04-07 2022-10-25 Saudi Arabian Oil Company Systems and methods for evaluating petroleum data for automated processes
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WO2022203702A1 (fr) * 2021-03-26 2022-09-29 Schlumberger Technology Corporation Adaptation de profondeur de données de puits de forage à l'aide d'algorithmes de point de changement

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