WO2016007808A1

WO2016007808A1 - Horizon clean-up

Info

Publication number: WO2016007808A1
Application number: PCT/US2015/039857
Authority: WO
Inventors: Stephen Robert Freeman
Original assignee: Schlumberger Canada Limited; Services Petroliers Schlumberger; Logined B.V.; Prad Research And Development Limited; Schlumberger Technology Corporation
Priority date: 2014-07-11
Filing date: 2015-07-10
Publication date: 2016-01-14
Also published as: EP3167153A1

Abstract

A method, apparatus, and program product clean seismic horizon interpretation data to better represent the geological form of the data, generally through the use of a combination of one or more computer-implemented algorithms indicative of data quality to assess the likely geological validity of the data. The processed data may be used with cut-offs to selectively remove poorer quality data and retain good quality data. In addition, distances from faults may be considered when applying the computer-implemented algorithms.

Description

HORIZON CLEAN-UP Cross-Reference to Related Applications

[0001] This application claims the filing benefit of U.S. Provisional Patent Application Serial No. 62/023,539 filed on July 11 , 2014, which is incorporated by reference herein in its entirety. Background

[0002] Seismic horizon interpretations are used in the oil & gas industry to build models of subsurface formations, which are in turn used to locate recoverable hydrocarbons and assist with the planning of drilling and other production operations. Horizons are generally considered to be surfaces in or between different types of rock or distinct layers of rock in a subsurface formation, and are in many cases represented by reflections found in seismic data collected from a seismic survey conducted for the subsurface formation. Seismic horizon interpretation attempts to identify these horizons in seismic data such that the horizons may be represented within a geological model of the subsurface formation and thereby accurately represent the various layers of rock within the subsurface formation. [0003] It has been found, however, that some seismic horizon interpretation data may not be of particularly high quality, and may not accurately reflect the actual geology of the subsurface formation. Moreover, it has been found that the quality of seismic horizon interpretation data in locations proximate to faults in the subsurface formation is generally of lower quality, and may lead to the creation of lower quality geological models. [0004] Therefore, a need exists in the art for a manner of improving the quality of seismic horizon interpretation data used in the creation of geological models. Summary

[0005] The embodiments disclosed herein provide a method, apparatus, and program product that clean seismic horizon interpretation data to better represent the geological form of the data, generally through the use of a combination of one or more analytical methods, also referred to herein as computer-implemented algorithms indicative of seismic horizon interpretation data quality, to assess the likely geological validity of the data. The processed data may be used with cut-offs to selectively remove poorer quality data and retain good quality data. In addition, distances from faults may be considered when applying the analytical methods/computer-implemented algorithms. [0006] Therefore, consistent with one aspect of the invention, seismic horizon interpretation data for a subsurface formation may be cleaned up by, using at least one processing unit, applying at least one computer-implemented algorithm indicative of seismic horizon interpretation data quality to the seismic horizon interpretation data based upon a distance of the seismic horizon interpretation data relative to at least one fault in the subsurface formation, and discarding a portion of the seismic horizon interpretation data based upon applying the at least one computer-implemented algorithm. [0007] These and other advantages and features, which characterize the invention, are set forth in the claims annexed hereto and forming a further part hereof. However, for a better understanding of the invention, and of the advantages and objectives attained through its use, reference should be made to the Drawings, and to the accompanying descriptive matter, in which there is described example embodiments of the invention. This summary is merely provided to introduce a selection of concepts that are further described below in the detailed description, and is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter. Brief Description of the Drawings

[0008] FIGURE 1 is a block diagram of an example hardware and software environment for a data processing system in accordance with implementation of various technologies and techniques described herein. [0009] FIGURES 2A-2D illustrate simplified, schematic views of an oilfield having subterranean formations containing reservoirs therein in accordance with implementations of various technologies and techniques described herein. [0010] FIGURE 3 illustrates a schematic view, partially in cross section of an oilfield having a plurality of data acquisition tools positioned at various locations along the oilfield for collecting data from the subterranean formations in accordance with implementations of various technologies and techniques described herein. [0011] FIGURE 4 illustrates a production system for performing one or more oilfield operations in accordance with implementations of various technologies and techniques described herein. [0012] FIGURE 5 illustrates a sequence of operations for an example horizon clean-up operation in accordance with implementations of various technologies and techniques described herein. [0013] FIGURES 6-10 illustrate an example user interface for use in performing an example horizon clean-up operation in accordance with implementations of various technologies and techniques described herein. [0014] FIGURE 11 illustrates an example visualization of previewed results for an example horizon clean-up operation in accordance with implementations of various technologies and techniques described herein. [0015] FIGURE 12 illustrates an example visualization of results for an example horizon clean-up operation in accordance with implementations of various technologies and techniques described herein. [0016] FIGURE 13 illustrates a comparison of example visualizations of geological models generated with and without the use of seismic horizon interpretation data processed using an example horizon clean-up operation in accordance with implementations of various technologies and techniques described herein. Detailed Description

[0017] The herein-described embodiments provide a method, apparatus, and program product that clean seismic horizon interpretation data to better represent the geological form of the data, generally through the use of a combination of one or more analytical methods, as well as relative distances to one or more faults, to assess the likely geological validity of the data. The processed data may then be used with cut-offs to selectively remove poorer quality data and retain good quality data, such that subsequent creation of a geological model may omit the poorer quality data during the creation process, thereby leading to a higher quality geological model. [0018] In some embodiments, for example, a method may be provided for cleaning up seismic horizon interpretation data for a subsurface formation. In the method, at least one processing unit is used to apply at least one computer- implemented algorithm indicative of seismic horizon interpretation data quality to the seismic horizon interpretation data based upon a distance of the seismic horizon interpretation data relative to at least one fault in the subsurface formation, and discard a portion of the seismic horizon interpretation data based upon applying the at least one computer-implemented algorithm. [0019] Some embodiments further include generating a geological model using the seismic horizon interpretation data after discarding the portion thereof, and some embodiments include receiving user input of at least one fault in the subsurface formation. Some embodiments include receiving user input of one or more computer- implemented algorithms to be applied and one or more cut-offs associated with the one or more computer-implemented algorithms to be applied, and some embodiments include receiving user input of one or more distances to the at least one fault. [0020] In some embodiments, the at least one computer-implemented algorithm is an edge detection algorithm, an anomaly identification algorithm, a surface stability algorithm or a confidence classification algorithm, and in some embodiments, the at least one computer-implemented algorithm aids in the identification of fault location, geometric anomalies or general geometric stability. [0021] Some embodiments further include receiving user input of one or more of a computer-implemented algorithm to be applied, a cut-off or a distance to a fault, generating a visual preview based upon the received user input, and modifying one or more of the computer-implemented algorithm to be applied, the cut-off or the distance to the fault in response to user input. In addition, in some embodiments, applying the at least one computer-implemented algorithm includes weighting the at least one computer-implemented algorithm based upon the distance relative to the at least one fault, while in some embodiments, applying the at least one computer-implemented algorithm includes normalizing a result of the at least one computer-implemented algorithm. In some embodiments, applying the at least one computer-implemented algorithm includes applying a plurality of computer-implemented algorithms indicative of seismic horizon interpretation data quality to the seismic horizon interpretation data and generating a combined attribute indicative of seismic horizon interpretation data quality by combining results of the plurality of computer-implemented algorithms. [0022] In addition, some embodiments include an apparatus with at least one processing unit and program code configured upon execution by the at least one processing unit to perform any of the aforementioned methods. Some embodiments also include a program product including a computer readable medium and program code stored on the computer readable medium and configured upon execution by at least one processing unit to perform any of the aforementioned methods. [0023] Other variations and modifications will be apparent to one of ordinary skill in the art. Hardware and Software Environment

[0024] Turning now to the drawings, wherein like numbers denote like parts throughout the several views, Fig. 1 illustrates an example data processing system 10 in which the various technologies and techniques described herein may be implemented. System 10 is illustrated as including one or more computers 12, e.g., client computers, each including a central processing unit (CPU) 14 including at least one hardware- based processor or processing core 16. CPU 14 is coupled to a memory 18, which may represent the random access memory (RAM) devices comprising the main storage of a computer 12, as well as any supplemental levels of memory, e.g., cache memories, non-volatile or backup memories (e.g., programmable or flash memories), read-only memories, etc. In addition, memory 18 may be considered to include memory storage physically located elsewhere in a computer 12, e.g., any cache memory in a

microprocessor or processing core, as well as any storage capacity used as a virtual memory, e.g., as stored on a mass storage device 20 or on another computer coupled to a computer 12. [0025] Each computer 12 also generally receives a number of inputs and outputs for communicating information externally. For interface with a user or operator, a computer 12 generally includes a user interface 22 incorporating one or more user input/output devices, e.g., a keyboard, a pointing device, a display, a printer, etc.

Otherwise, user input may be received, e.g., over a network interface 24 coupled to a network 26, from one or more external computers, e.g., one or more servers 28 or other computers 12. A computer 12 also may be in communication with one or more mass storage devices 20, which may be, for example, internal hard disk storage devices, external hard disk storage devices, storage area network devices, etc. [0026] A computer 12 generally operates under the control of an operating system 30 and executes or otherwise relies upon various computer software

applications, components, programs, objects, modules, data structures, etc. For example, a petro-technical module or component 32 executing within an exploration and production (E&P) platform 34 may be used to access, process, generate, modify or otherwise utilize petro-technical data, e.g., as stored locally in a database 36 and/or accessible remotely from a collaboration platform 38. Collaboration platform 38 may be implemented using multiple servers 28 in some implementations, and it will be appreciated that each server 28 may incorporate a CPU, memory, and other hardware components similar to a computer 12. [0027] In one non-limiting embodiment, for example, E&P platform 34 may implemented as the PETREL Exploration & Production (E&P) software platform, while collaboration platform 38 may be implemented as the STUDIO E&P KNOWLEDGE ENVIRONMENT platform, both of which are available from Schlumberger Ltd. and its affiliates. It will be appreciated, however, that the techniques discussed herein may be utilized in connection with other platforms and environments, so the invention is not limited to the particular software platforms and environments discussed herein. [0028] In general, the routines executed to implement the embodiments disclosed herein, whether implemented as part of an operating system or a specific application, component, program, object, module or sequence of instructions, or even a subset thereof, will be referred to herein as "computer program code," or simply "program code." Program code generally comprises one or more instructions that are resident at various times in various memory and storage devices in a computer, and that, when read and executed by one or more hardware-based processing units in a computer (e.g., microprocessors, processing cores, or other hardware-based circuit logic), cause that computer to perform the steps embodying desired functionality.

Moreover, while embodiments have and hereinafter will be described in the context of fully functioning computers and computer systems, those skilled in the art will appreciate that the various embodiments are capable of being distributed as a program product in a variety of forms, and that the invention applies equally regardless of the particular type of computer readable media used to actually carry out the distribution. [0029] Such computer readable media may include computer readable storage media and communication media. Computer readable storage media is non-transitory in nature, and may include volatile and non-volatile, and removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules or other data.

Computer readable storage media may further include RAM, ROM, erasable

programmable read-only memory (EPROM), electrically erasable programmable read- only memory (EEPROM), flash memory or other solid state memory technology, CD- ROM, DVD, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and which can be accessed by computer 10.

Communication media may embody computer readable instructions, data structures or other program modules. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above may also be included within the scope of computer readable media. [0030] Various program code described hereinafter may be identified based upon the application within which it is implemented in a specific embodiment of the invention. However, it should be appreciated that any particular program nomenclature that follows is used merely for convenience, and thus the invention should not be limited to use solely in any specific application identified and/or implied by such nomenclature. Furthermore, given the endless number of manners in which computer programs may be organized into routines, procedures, methods, modules, objects, and the like, as well as the various manners in which program functionality may be allocated among various software layers that are resident within a typical computer (e.g., operating systems, libraries, API's, applications, applets, etc.), it should be appreciated that the invention is not limited to the specific organization and allocation of program functionality described herein. [0031] Furthermore, it will be appreciated by those of ordinary skill in the art having the benefit of the instant disclosure that the various operations described herein that may be performed by any program code, or performed in any routines, workflows, or the like, may be combined, split, reordered, omitted, and/or supplemented with other techniques known in the art, and therefore, the invention is not limited to the particular sequences of operations described herein. [0032] Those skilled in the art will recognize that the example environment illustrated in Fig. 1 is not intended to limit the invention. Indeed, those skilled in the art will recognize that other alternative hardware and/or software environments may be used without departing from the scope of the invention. Oilfield Operations [0033] Figs. 2A-2D illustrate simplified, schematic views of an oilfield 100 having subterranean formation 102 containing reservoir 104 therein in accordance with implementations of various technologies and techniques described herein. Fig. 2A illustrates a survey operation being performed by a survey tool, such as seismic truck 106.1 , to measure properties of the subterranean formation. The survey operation is a seismic survey operation for producing sound vibrations. In Fig. 2A, one such sound vibration, sound vibration 112 generated by source 110, reflects off horizons 114 in earth formation 116. A set of sound vibrations is received by sensors, such as geophone-receivers 118, situated on the earth's surface. The data received 120 is provided as input data to a computer 122.1 of a seismic truck 106.1 , and responsive to the input data, computer 122.1 generates seismic data output 124. This seismic data output may be stored, transmitted or further processed as desired, for example, by data reduction. [0034] Fig. 2B illustrates a drilling operation being performed by drilling tools 106.2 suspended by rig 128 and advanced into subterranean formations 102 to form wellbore 136. Mud pit 130 is used to draw drilling mud into the drilling tools via flow line 132 for circulating drilling mud down through the drilling tools, then up wellbore 136 and back to the surface. The drilling mud may be filtered and returned to the mud pit. A circulating system may be used for storing, controlling, or filtering the flowing drilling muds. The drilling tools are advanced into subterranean formations 102 to reach reservoir 104. Each well may target one or more reservoirs. The drilling tools are adapted for measuring downhole properties using logging while drilling tools. The logging while drilling tools may also be adapted for taking core sample 133 as shown. [0035] Computer facilities may be positioned at various locations about the oilfield 100 (e.g., the surface unit 134) and/or at remote locations. Surface unit 134 may be used to communicate with the drilling tools and/or offsite operations, as well as with other surface or downhole sensors. Surface unit 134 is capable of communicating with the drilling tools to send commands to the drilling tools, and to receive data therefrom. Surface unit 134 may also collect data generated during the drilling operation and produces data output 135, which may then be stored or transmitted. [0036] Sensors (S), such as gauges, may be positioned about oilfield 100 to collect data relating to various oilfield operations as described previously. As shown, sensor (S) is positioned in one or more locations in the drilling tools and/or at rig 128 to measure drilling parameters, such as weight on bit, torque on bit, pressures,

temperatures, flow rates, compositions, rotary speed, and/or other parameters of the field operation. Sensors (S) may also be positioned in one or more locations in the circulating system. [0037] Drilling tools 106.2 may include a bottom hole assembly (BHA) (not shown), generally referenced, near the drill bit (e.g., within several drill collar lengths from the drill bit). The bottom hole assembly includes capabilities for measuring, processing, and storing information, as well as communicating with surface unit 134. The bottom hole assembly further includes drill collars for performing various other measurement functions. [0038] The bottom hole assembly may include a communication subassembly that communicates with surface unit 134. The communication subassembly is adapted to send signals to and receive signals from the surface using a communications channel such as mud pulse telemetry, electro-magnetic telemetry, or wired drill pipe

communications. The communication subassembly may include, for example, a transmitter that generates a signal, such as an acoustic or electromagnetic signal, which is representative of the measured drilling parameters. It will be appreciated by one of skill in the art that a variety of telemetry systems may be employed, such as wired drill pipe, electromagnetic or other known telemetry systems. [0039] Generally, the wellbore is drilled according to a drilling plan that is established prior to drilling. The drilling plan sets forth equipment, pressures, trajectories and/or other parameters that define the drilling process for the wellsite. The drilling operation may then be performed according to the drilling plan. However, as information is gathered, the drilling operation may need to deviate from the drilling plan. Additionally, as drilling or other operations are performed, the subsurface conditions may change. The earth model may also need adjustment as new information is collected [0040] The data gathered by sensors (S) may be collected by surface unit 134 and/or other data collection sources for analysis or other processing. The data collected by sensors (S) may be used alone or in combination with other data. The data may be collected in one or more databases and/or transmitted on or offsite. The data may be historical data, real time data, or combinations thereof. The real time data may be used in real time, or stored for later use. The data may also be combined with historical data or other inputs for further analysis. The data may be stored in separate databases, or combined into a single database. [0041] Surface unit 134 may include transceiver 137 to allow communications between surface unit 134 and various portions of the oilfield 100 or other locations. Surface unit 134 may also be provided with or functionally connected to one or more controllers (not shown) for actuating mechanisms at oilfield 100. Surface unit 134 may then send command signals to oilfield 100 in response to data received. Surface unit 134 may receive commands via transceiver 137 or may itself execute commands to the controller. A processor may be provided to analyze the data (locally or remotely), make the decisions and/or actuate the controller. In this manner, oilfield 100 may be selectively adjusted based on the data collected. This technique may be used to optimize portions of the field operation, such as controlling drilling, weight on bit, pump rates, or other parameters. These adjustments may be made automatically based on computer protocol, and/or manually by an operator. In some cases, well plans may be adjusted to select optimum operating conditions, or to avoid problems. [0042] Fig. 2C illustrates a wireline operation being performed by wireline tool 106.3 suspended by rig 128 and into wellbore 136 of Fig. 2B. Wireline tool 106.3 is adapted for deployment into wellbore 136 for generating well logs, performing downhole tests and/or collecting samples. Wireline tool 106.3 may be used to provide another method and apparatus for performing a seismic survey operation. Wireline tool 106.3 may, for example, have an explosive, radioactive, electrical, or acoustic energy source 144 that sends and/or receives electrical signals to surrounding subterranean formations 102 and fluids therein. [0043] Wireline tool 106.3 may be operatively connected to, for example, geophones 118 and a computer 122.1 of a seismic truck 106.1 of Fig. 2A. Wireline tool 106.3 may also provide data to surface unit 134. Surface unit 134 may collect data generated during the wireline operation and may produce data output 135 that may be stored or transmitted. Wireline tool 106.3 may be positioned at various depths in the wellbore 136 to provide a survey or other information relating to the subterranean formation 102. [0044] Sensors (S), such as gauges, may be positioned about oilfield 100 to collect data relating to various field operations as described previously. As shown, sensor S is positioned in wireline tool 106.3 to measure downhole parameters which relate to, for example porosity, permeability, fluid composition and/or other parameters of the field operation. [0045] Fig. 2D illustrates a production operation being performed by production tool 106.4 deployed from a production unit or Christmas tree 129 and into completed wellbore 136 for drawing fluid from the downhole reservoirs into surface facilities 142. The fluid flows from reservoir 104 through perforations in the casing (not shown) and into production tool 106.4 in wellbore 136 and to surface facilities 142 via gathering network 146. [0046] Sensors (S), such as gauges, may be positioned about oilfield 100 to collect data relating to various field operations as described previously. As shown, the sensor (S) may be positioned in production tool 106.4 or associated equipment, such as christmas tree 129, gathering network 146, surface facility 142, and/or the production facility, to measure fluid parameters, such as fluid composition, flow rates, pressures, temperatures, and/or other parameters of the production operation. [0047] Production may also include injection wells for added recovery. One or more gathering facilities may be operatively connected to one or more of the wellsites for selectively collecting downhole fluids from the wellsite(s). [0048] While Figs. 2B-2D illustrate tools used to measure properties of an oilfield, it will be appreciated that the tools may be used in connection with non-oilfield operations, such as gas fields, mines, aquifers, storage, or other subterranean facilities. Also, while certain data acquisition tools are depicted, it will be appreciated that various measurement tools capable of sensing parameters, such as seismic two-way travel time, density, resistivity, production rate, etc., of the subterranean formation and/or its geological formations may be used. Various sensors (S) may be located at various positions along the wellbore and/or the monitoring tools to collect and/or monitor the desired data. Other sources of data may also be provided from offsite locations. [0049] The field configurations of Figs. 2A-2D are intended to provide a brief description of an example of a field usable with oilfield application frameworks. Part, or all, of oilfield 100 may be on land, water, and/or sea. Also, while a single field measured at a single location is depicted, oilfield applications may be utilized with any combination of one or more oilfields, one or more processing facilities and one or more wellsites. [0050] Fig. 3 illustrates a schematic view, partially in cross section of oilfield 200 having data acquisition tools 202.1 , 202.2, 202.3 and 202.4 positioned at various locations along oilfield 200 for collecting data of subterranean formation 204 in accordance with implementations of various technologies and techniques described herein. Data acquisition tools 202.1 -202.4 may be the same as data acquisition tools 106.1 -106.4 of Figs. 2A-2D, respectively, or others not depicted. As shown, data acquisition tools 202.1 -202.4 generate data plots or measurements 208.1 -208.4, respectively. These data plots are depicted along oilfield 200 to demonstrate the data generated by the various operations. [0051] Data plots 208.1 -208.3 are examples of static data plots that may be generated by data acquisition tools 202.1 -202.3, respectively, however, it should be understood that data plots 208.1 -208.3 may also be data plots that are updated in real time. These measurements may be analyzed to better define the properties of the formation(s) and/or determine the accuracy of the measurements and/or for checking for errors. The plots of each of the respective measurements may be aligned and scaled for comparison and verification of the properties. [0052] Static data plot 208.1 is a seismic two-way response over a period of time. Static plot 208.2 is core sample data measured from a core sample of the formation 204. The core sample may be used to provide data, such as a graph of the density, porosity, permeability, or some other physical property of the core sample over the length of the core. Tests for density and viscosity may be performed on the fluids in the core at varying pressures and temperatures. Static data plot 208.3 is a logging trace that generally provides a resistivity or other measurement of the formation at various depths. [0053] A production decline curve or graph 208.4 is a dynamic data plot of the fluid flow rate over time. The production decline curve generally provides the production rate as a function of time. As the fluid flows through the wellbore, measurements are taken of fluid properties, such as flow rates, pressures, composition, etc. [0054] Other data may also be collected, such as historical data, user inputs, economic information, and/or other measurement data and other parameters of interest. As described below, the static and dynamic measurements may be analyzed and used to generate models of the subterranean formation to determine characteristics thereof. Similar measurements may also be used to measure changes in formation aspects over time. [0055] The subterranean structure 204 has a plurality of geological formations 206.1 -206.4. As shown, this structure has several formations or layers, including a shale layer 206.1 , a carbonate layer 206.2, a shale layer 206.3 and a sand layer 206.4. A fault 207 extends through the shale layer 206.1 and the carbonate layer 206.2. The static data acquisition tools are adapted to take measurements and detect characteristics of the formations. [0056] While a specific subterranean formation with specific geological structures is depicted, it will be appreciated that oilfield 200 may contain a variety of geological structures and/or formations, sometimes having extreme complexity. In some locations, generally below the water line, fluid may occupy pore spaces of the formations. Each of the measurement devices may be used to measure properties of the formations and/or its geological features. While each acquisition tool is shown as being in specific locations in oilfield 200, it will be appreciated that one or more types of measurement may be taken at one or more locations across one or more fields or other locations for comparison and/or analysis. [0057] The data collected from various sources, such as the data acquisition tools of Fig. 3, may then be processed and/or evaluated. Generally, seismic data displayed in static data plot 208.1 from data acquisition tool 202.1 is used by a geophysicist to determine characteristics of the subterranean formations and features. The core data shown in static plot 208.2 and/or log data from well log 208.3 are generally used by a geologist to determine various characteristics of the subterranean formation. The production data from graph 208.4 is generally used by the reservoir engineer to determine fluid flow reservoir characteristics. The data analyzed by the geologist, geophysicist and the reservoir engineer may be analyzed using modeling techniques. [0058] Fig. 4 illustrates an oilfield 300 for performing production operations in accordance with implementations of various technologies and techniques described herein. As shown, the oilfield has a plurality of wellsites 302 operatively connected to central processing facility 354. The oilfield configuration of Fig. 4 is not intended to limit the scope of the oilfield application system. Part or all of the oilfield may be on land and/or sea. Also, while a single oilfield with a single processing facility and a plurality of wellsites is depicted, any combination of one or more oilfields, one or more processing facilities and one or more wellsites may be present. [0059] Each wellsite 302 has equipment that forms wellbore 336 into the earth. The wellbores extend through subterranean formations 306 including reservoirs 304. These reservoirs 304 contain fluids, such as hydrocarbons. The wellsites draw fluid from the reservoirs and pass them to the processing facilities via surface networks 344. The surface networks 344 have tubing and control mechanisms for controlling the flow of fluids from the wellsite to processing facility 354. Horizon Clean-Up

[0060] Embodiments consistent with the invention generally provide a method to clean seismic horizon data to better represent the geological form of the data. In such embodiments, a series of one or more analytical methods is used to assess the likely geological validity of the data. That information is then used with cut-offs to selectively remove poorer quality data and retain good quality data. In some embodiments, for example, a series of computer-implemented algorithms, e.g., the edge detection, anomaly identifier, surface stability, and/or confidence classification algorithms available in the aforementioned PETREL platform, may be used to identify more or less geologically valid seismic horizon interpretation data. The series of computer- implemented algorithms may also be combined with a knowledge of the 3D distance of seismic horizon data from faults (if that analysis style is used) to weight the relative importance of applying the different algorithm types and cut-offs. Further, in some embodiments, a test may be applied that tests the elevation of a horizon relative to an expected elevation for a given side of a fault (if that option is selected). Thus, rather than simply deleting or removing data purely based upon its distance from a fault in the subsurface formation, one or more attributes that are indicative of seismic horizon data quality may also be used, thereby providing a more intelligent assessment of the quality of seismic horizon data. [0061] It will be appreciated that practically any computer-implemented algorithm that is indicative of the quality of seismic horizon data may be used, so the invention is not limited to the specific algorithms discussed herein. One confidence classification algorithm, for example, may include a surface stability index and RMS amplitude calculation, and may analyze surface stability of a pick along with a seismic response in and around the pick to produce a confidence estimate of a horizon. One edge detection algorithm may compute a number of geometric calculations over a range of local neighborhood searches to identify low throw structures and identify high edge values indicative of unstable or faulted areas. One anomaly identifier algorithm may identify local geometric anomalies in horizon data, such as data on the wrong side of a fault or on the wrong seismic reflector, and may be provided with a search distance setting to limit the distance over which the algorithm searches for anomalous data. One surface difference algorithm may test the consistency of the elevation data of a horizon adjacent to faults relative to the broader neighborhood, and taking into account the fault location. This can aid in the rapid discrimination of data whether it is located on the correct side of the fault, as removal of horizon data on the wrong side of a fault can be important in some embodiments for developing a robust geological model. [0062] Each computer-implemented algorithm may, in some embodiments, output an attribute for each data point in the seismic horizon data representative of data quality, e.g., a confidence level between 0 and 100%. It will also be appreciated that the outputs of the computer-implemented algorithms may be combined and/or normalized to generate a“combined” attribute representative of data quality.

Thereafter, a filter may be applied to discard any data having a confidence level below a cut-off, with the retained data thereafter used for subsequent modeling, e.g., to constrain a horizontal population process, thereby resulting in a more consistent model. [0063] Fig. 5, for example, illustrates an example sequence of operations 400 constituting a workflow that may be implemented by one or more routines executed in apparatus 10 of Fig. 1 , e.g., within one or more petro-technical modules 32. As illustrated in Fig. 5, upon selection of a desired clean-up method by a user (block 402), embodiments consistent with the invention may accept a series of data types that represent seismic horizon interpretations (e.g., seismic horizon interpretation grids, surfaces and points) (block 404), as well as additional data such as one or more fault models (e.g., structural frameworks and/or pillar grids) (block 406). A series of computer-implemented algorithms that aid in the identification of fault location, geometric anomalies and general geometric stability across the horizons may then be selected (block 408), along with cut-offs, and distances around faults (block 410). Then as illustrated in block 412, based upon receipt of these selections from a user, the selected algorithms may be applied, with the results normalized to consistently identify more versus less geologically viable results. Cut-offs may then be applied to the individual solutions to provide an indication of more versus less valid seismic horizon interpretation data. If faults are provided, for example, a series of weights may also be applied on the horizon results to delete less valid data in the proximity of the faults, based upon the distance selections received from a user. [0064] Next, in block 414, a visual inspection of the potential solution may be presented prior to further processing, at which point a user may, if not yet satisfied with the results (block 416) manipulate algorithms, cut-offs and/or distances (block 418) and return control to block 412 to apply the manipulated algorithms, cut-offs and/or distances, and such that the sequence of blocks 412-418 may be repeated until an acceptable solution is obtained. Once an acceptable solution is obtained, block 416 may pass control to block 420, and a user may then apply the algorithms, cut-offs, and distances to generate a new,“cleaned” set of seismic horizon interpretation data. Then, in some embodiments, the cleaned set of seismic horizon interpretation data may be used to create a geological model, as illustrated in block 422. Workflow 400 is then complete. [0065] The aforementioned workflow may be used to perform general cleaning of seismic horizon data in some embodiments. Moreover, in some embodiments, the workflow may be used to clean data around faults to aid in the creation of geological models (e.g., pillar grids or structural framework models). [0066] Accordingly, in some of the embodiments discussed herein, a series of one or more user-selected computer-implemented algorithms that are capable of distinguishing between“good” versus“bad” seismic horizon interpretation data is applied to a set of seismic horizon interpretation data, and the results are normalized, combined, and weighted around faults based upon distance-based thresholds set by a user. User-selected cut-offs are then used on the combined information and the results are presented visually to suggest to the user areas of the seismic horizon interpretation data that the user may wish to delete or retain. The user may then, e.g., in an interactive fashion, modify any of the user-selected parameters and view the modified results, so that when acceptable results are obtained the user may delete the data identified for deletion and thereby clean the set of seismic horizon interpretation data. Accordingly, through this process, the“bad” seismic horizon interpretation data may be targeted and selectively deleted, with the“good” seismic horizon interpretation data retained for further use, e.g., in connection with creating a geological model. [0067] Figs. 6-13 next illustrate via screenshots an example horizon clean-up operation consistent with the invention, implemented within the aforementioned

PETREL software platform, e.g., using a dialog box 500 including a plurality of user controls, e.g., a collection of tabs, panels, radio buttons, buttons, dropdown buttons, text boxes, checkboxes and any other types of user controls as will be appreciated by those of ordinary skill in the art having the benefit of the instant disclosure. In the illustrated embodiment, a panel 502 is displayed throughout the user interaction, including options for the visible attribute (“keep vs. delete”, distance to fault, zone type, nearest fault, etc.) and for visible data (selecting one or more zones, e.g., zones 1 -3, and one or more actions, e.g., to delete and/or to delete). In addition, buttons are provided for resetting the operation (Reset), previewing the output (Preview Output), applying settings changes (Apply), performing the operation (OK) and canceling the operation (Cancel). [0068] In step 1 (Fig. 6), a user may, on a settings tab 504, select various settings for use in a clean-up operation, e.g., to select between creating a new output or overwriting an existing output (panel 506), select a horizon clean-up cleaning method to be performed (panel 508), and select a polygon region (panel 510). On panel 508, a user may be presented with options for selecting a near-fault areas clean-up method and/or a general areas clean-up method, and under the near-fault areas clean-up method, a user may select an average interval velocity (m/s) based on distance from fault or distance from fault along with attributes, as well as surface difference and outputting quality control results. In panel 510, a user may select performing the operation inside a polygon, as well as specify a polyline region. [0069] Next, in step 2 (Fig. 7), on a horizon input tab 512, a panel 514 may be provided to enable a user to supply the seismic horizon interpretation data to be cleaned (or points/surface). Options may be provided to open data via an open file dialog, select whether to copy properties to the output, and select surface creation defaults, and the selected data may be displayed in a table having horizon, use, settings and view columns, with a checkbox provided in each row of the use column to enable the data for the corresponding row to be used in the operation, and with settings and view buttons provided for each row in the settings and view columns to enable any additional settings for that data to be configured and/or to enable that data to be viewed. Fig. 7, for example, shows an“Upper_Res_Fault_Cut” data set including horizon interpretation data labeled as“Survey 1”. It will be appreciated that in various

embodiments the data may include points, 3D seismic horizons and/or surfaces. [0070] Next, in step 3 (Fig. 8), on a fault input tab 516, a panel 518 may be provided to enable a user to supply the faults around which to clean the seismic horizon interpretation data. The selected data may be displayed in a table having fault name, use, side 1 and side 2 columns, with a checkbox provided in each row of the use column to enable the data for the corresponding row to be used in the operation, and with view buttons provided for side 1 and side 2 (the front and back of the fault) of the corresponding fault. The view buttons allow the user to interactively view the data of interest for processing. Fig. 8, for example, shows 13 faults to be used in the operation. It will be appreciated that in various embodiments the data may include structural framework faults, faults in a pillar grid, etc. [0071] Next, in step 4 (Fig. 9), on a fault algorithms tab 520, a panel 522 may be provided to enable a user to select one or more computer-implemented

algorithms/attributes and one or more cut-offs, with defaults provided in some instances. A button may be provided to add an algorithm, with a table also provided listing each selected algorithm (attribute), along with columns for changing settings, selecting an operator, and selecting various additional settings common to those algorithms, e.g., operator, which specifies the mathematical operator to apply to the results of the algorithm (e.g. greater than, less than), the filter value, which allows the user to specify the range of data to remove or retain per algorithm (e.g. remove the 1 % most extreme values)) and apply per patch, which allows the algorithms to be applied independently for different sides of the faults. Additional columns may also be provided in other embodiments. [0072] The attribute column provides a drop down button to select between different computer-implemented cleaning algorithms (e.g., confidence classification, anomaly identification, edge detection, surface stability, etc.). The settings column includes settings buttons that open additional dialog boxes to input algorithm-specific settings. As an example, for confidence classification, settings such as stability window radius and thick-slice volume amplitude parameters (e.g., seismic cube and sample window) may be input via a separate dialog box). The operator column includes operator dropdown buttons enabling selection of different operators (e.g., less than, less than or equal to, equal to, greater than, greater than or equal to, etc.). [0073] Next, in step 5 (Fig. 10), on a cleaning areas tab 524, a panel 526 may be provided to enable a user to choose distances around the faults to weight each selected algorithm. Checkboxes may be provided to select whether or not to use defaults and whether to use different sides, and zone distances may be input via textboxes (e.g., distances of 50 and 200 for zones 1 and 2 in Fig. 10). A table may be presented including distances for all faults, for individual faults, and for different horizon interpretation data (e.g., Survey 1 ). View buttons may also be presented in Side 1 and/or Side 2 columns to view associated data. [0074] Next, in step 6, a user may be allowed to preview the results, e.g., by selecting“Preview Output” button in panel 502. Fig. 11 , for example, illustrates example previewed results in a visualization 530, where the lighter shading is used to represent data to be potentially deleted or removed, and the darker shading is used to represent data to be potentially retained. A legend 532 (indicating, from top to bottom, undetermined, keep and discard) may be used to map the shading, and a plurality of toolbar buttons 534 may be provided to enable a user to move around the visualization and/or modify the visualization, in a manner that will be apparent to one of ordinary skill in the art having the benefit of the instant disclosure. [0075] Returning to Fig. 10, after previewing the data, the user may be permitted to modify any of the aforementioned selections and preview modified results until an acceptable result is achieved. Subsequently, the user may select the“OK” button in panel 502 and initiate performance of the horizon clean-up operation. Fig. 12, for example, displays an example visualization 540 of a horizon clean-up operation, with a legend 542 representative of the attribute values of the computed parameters that are used to choose to retain or remove data, and with a plurality of toolbar buttons 534 provided to enable a user to move around and/or modify the visualization. [0076] When the horizon has been cleaned around the faults in the manner described herein, the data may then be used by a geomodel creation method (e.g., horizon population in pillar grids or structural framework modeling) to produce well constrained, high confidence models. For example, as illustrated in Fig. 13, when comparing a geological model created with lower quality seismic horizon interpretation data (as shown in visualization 550 in the top left of Fig. 13) with the same model created using data cleaned up using the aforementioned horizon clean-up process (as shown in visualization 552 in the bottom right of Fig. 13), the horizon generally omits unstable data as well as data in the immediate vicinity of faults, resulting in improved model quality. [0077] Embodiments of the invention therefore allow for the selective deleting of erroneous data points while retaining stable data, in many cases allowing for high quality data to be retained in a modeling workflow close into a fault, and thereby improving control during geomodeling, and ultimately leading to more well constrained geological models. Such embodiments therefore address a technical problem

presented by the presence of poor quality data in seismic horizon interpretation data proximate faults, and do so through a technical solution in which such poor quality data is automatically identified and discarded through the automated application of one or more data quality-related algorithms constrained by distance from one or more faults. [0078] While particular embodiments have been described, it is not intended that the invention be limited thereto, as it is intended that the invention be as broad in scope as the art will allow and that the specification be read likewise. It will therefore be appreciated by those skilled in the art that yet other modifications could be made without deviating from its spirit and scope as claimed.

Claims

What is claimed is: 1. A method of cleaning up seismic horizon interpretation data for a subsurface formation, the method comprising:

using at least one processing unit, applying at least one computer- implemented algorithm indicative of seismic horizon interpretation data quality to the seismic horizon interpretation data based upon a distance of the seismic horizon interpretation data relative to at least one fault in the subsurface formation; and

discarding a portion of the seismic horizon interpretation data based upon applying the at least one computer-implemented algorithm.

2. The method of claim 1 , further comprising generating a geological model using the seismic horizon interpretation data after discarding the portion thereof.

3. The method of claim 1 , further comprising receiving user input of at least one fault in the subsurface formation.

4. The method of claim 1 , further comprising receiving user input of one or more computer-implemented algorithms to be applied and one or more cut-offs associated with the one or more computer-implemented algorithms to be applied.

5. The method of claim 1 , further comprising receiving user input of one or more distances to the at least one fault.

6. The method of claim 1 , wherein the at least one computer-implemented algorithm is an edge detection algorithm, an anomaly identification algorithm, a surface stability algorithm or a confidence classification algorithm.

7. The method of claim 1 , wherein the at least one computer-implemented algorithm aids in the identification of fault location, geometric anomalies or general geometric stability.

8. The method of claim 1 , further comprising:

receiving user input of one or more of a computer-implemented algorithm to be applied, a cut-off or a distance to a fault;

generating a visual preview based upon the received user input; and modifying one or more of the computer-implemented algorithm to be applied, the cut-off or the distance to the fault in response to user input.

9. The method of claim 1 , wherein applying the at least one computer- implemented algorithm includes weighting the at least one computer-implemented algorithm based upon the distance relative to the at least one fault.

10. The method of claim 1 , wherein applying the at least one computer- implemented algorithm includes normalizing a result of the at least one computer- implemented algorithm.

11. The method of claim 1 , wherein applying the at least one computer- implemented algorithm includes applying a plurality of computer-implemented

algorithms indicative of seismic horizon interpretation data quality to the seismic horizon interpretation data and generating a combined attribute indicative of seismic horizon interpretation data quality by combining results of the plurality of computer-implemented algorithms.

12. An apparatus, comprising:

at least one processing unit; and

program code configured upon execution by the at least one processing unit to clean up seismic horizon interpretation data for a subsurface formation by:

applying at least one computer-implemented algorithm indicative of seismic horizon interpretation data quality to the seismic horizon interpretation data based upon a distance of the seismic horizon interpretation data relative to at least one fault in the subsurface formation; and

13. The apparatus of claim 12, wherein the program code is further configured to generate a geological model using the seismic horizon interpretation data after discarding the portion thereof.

14. The apparatus of claim 12, wherein the at least one computer-implemented algorithm is an edge detection algorithm, an anomaly identification algorithm, a surface stability algorithm or a confidence classification algorithm.

15. The apparatus of claim 12, wherein the at least one computer-implemented algorithm aids in the identification of fault location, geometric anomalies or general geometric stability.

16. The apparatus of claim 12, wherein the program code is further configured to receive user input of one or more of a computer-implemented algorithm to be applied, a cut-off or a distance to a fault, generate a visual preview based upon the received user input, and modify one or more of the computer-implemented algorithm to be applied, the cut-off or the distance to the fault in response to user input.

17. The apparatus of claim 12, wherein the program code is configured to apply the at least one computer-implemented algorithm by weighting the at least one computer-implemented algorithm based upon the distance relative to the at least one fault.

18. The apparatus of claim 12, wherein the program code is configured to apply the at least one computer-implemented algorithm by normalizing a result of the at least one computer-implemented algorithm.

19. The apparatus of claim 12, wherein the program code is configured to apply the at least one computer-implemented algorithm by applying a plurality of computer- implemented algorithms indicative of seismic horizon interpretation data quality to the seismic horizon interpretation data and generating a combined attribute indicative of seismic horizon interpretation data quality by combining results of the plurality of computer-implemented algorithms.

20. A program product, comprising:

a computer readable medium; and

program code stored on the computer readable medium and configured upon execution by at least one processing unit to clean up seismic horizon interpretation data for a subsurface formation by:

applying at least one computer-implemented algorithm indicative of seismic horizon interpretation data quality to the seismic horizon

interpretation data based upon a distance of the seismic horizon

interpretation data relative to at least one fault in the subsurface formation; and