WO2015199727A1 - Découverte de défauts dans des données géologiques - Google Patents

Découverte de défauts dans des données géologiques Download PDF

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Publication number
WO2015199727A1
WO2015199727A1 PCT/US2014/044632 US2014044632W WO2015199727A1 WO 2015199727 A1 WO2015199727 A1 WO 2015199727A1 US 2014044632 W US2014044632 W US 2014044632W WO 2015199727 A1 WO2015199727 A1 WO 2015199727A1
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WO
WIPO (PCT)
Prior art keywords
tensor
image
fault
semblance
point
Prior art date
Application number
PCT/US2014/044632
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English (en)
Inventor
Gregory Stuart Snider
Original Assignee
Hewlett-Packard Development Company, L.P.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hewlett-Packard Development Company, L.P. filed Critical Hewlett-Packard Development Company, L.P.
Priority to PCT/US2014/044632 priority Critical patent/WO2015199727A1/fr
Publication of WO2015199727A1 publication Critical patent/WO2015199727A1/fr

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/30—Analysis
    • G01V1/301—Analysis for determining seismic cross-sections or geostructures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/64—Geostructures, e.g. in 3D data cubes
    • G01V2210/642—Faults

Definitions

  • ⁇ OOOiJ Geological data may be analyzed to detect a geological fault.
  • a geoiogical fault is a crack in the earth's crust resulting from a displacement of one !andmass relative to another.
  • Such analysis of geological data may be utilized in oil and gas exploration.
  • Figure 1 is a functional block diagram illustrating one example of a system for finding faults in geological data.
  • FIG. 2 is another functional block diagram illustrating one example of a system for finding faults in geological data.
  • Figure 3 illustrates one example fo finding faults in geological data.
  • Figure 4 is a block diagram illustrating one example of a processing system for finding faults in geologicai data.
  • Figure 5 is a block diagram illustrating one example of a computer readable medium for finding faults in geological data.
  • Figure 8 is a flow diagram illustrating one example of a method for finding faults in geological data.
  • Oil and gas exploration utilizes analysis of selsmological data to detect a potential geological fault.
  • the amount of data extracted from a surve of a potential oii field is large, and a fraction of that data is analyzed, resulting in possible errors in fault detection.
  • Local semblance analysis may be performed on geological data to detect a potential geological fault. Output of the semblance analysis may be noisy, and in some instances, the output may not clearly delineate a potential: geographical fault.
  • One example is a system that combines the semblance analysis with a filtering step to enhance data related to a potential geological fault.
  • the example system comprises a semblance analyzer, a tensor field extractor, a phase congruence filter, and an evaluator.
  • Input geological Image is received.
  • the semblance analyzer generates a semblance metric at each point of the geological image, the semblance metric being indicative of local semblance analysis.
  • the tensor field extractor extracts a structure tensor at each point of the geoiogicai image, the structure tensor being indicative of local structure in the geological image.
  • the phase congruency filter provides a stick tensor at each point of the geological image, the stick tensor being based on the semblance metric, and being indicative of a point lying on a major edge in the geoiogicai image.
  • the evaluator evaluates, at each point of the geological image, a fault tensor that is based on the semblance metric, the structure tensor, and the stick tensor, the fault tensor being indicative of a likelihood of each point lying along a potential fault in the geological image. In particular, it interpolates fragmentary traces to provide smooth curves indicating potential faults.
  • FIG. 1 is a functional block diagram illustrating one example of a system 100 for finding faults in geological data.
  • the system 100 receives input geological data via a processing system
  • a processing system is a combination of machines that may be utilized to receive an input, process the input, and generate an output, in one exampie, the input geoiogicai data is processed by a sembiance analyzer and a tensor field extractor included in system 100, The semblance anaiyzer generates a sembiance metric at each point of the geoiogicai image, the sembiance metric indicative of local sembiance analysis.
  • the tensor field extractor extracts a structure tensor at each point of the geoiogicai image, the structure tensor indicative of locai structure in the geoiogicai image.
  • the semblance metric is processed by a phase congruency filter to provide a stick tensor at each point of the geoiogicai image, the stick tensor indicative of a point tying on a major edge in the geoiogicai image.
  • the sembiance metric, structure tensor, and the stick tensor are processed by an evaluator to determine a fauit tensor, the fault tensor indicative of a likelihood of each point lying along a potential fault in the geoiogicai image,
  • f 0012 J System 100 includes input geological data 102, a semblance anaiyzer 104, a tensor field extractor 108, a phase congruency fitter 108, a stick tensor 110, and an evaluator 112.
  • input geoiogicai data 102 is a geological image representing data related to geoiogicai strata
  • input geological data 102 is a geological image representing
  • seismological data Such data may be received, for exampie, from a variety of data acquisition systems.
  • the input geoiogicai data 102 may be a three dimensional image.
  • the sembiance anaiyzer 104 performs semblance analysis on the input geoiogicai data 102 to generate a sembiance metric at each point of the geoiogicai data.
  • the sembiance metric is a quantitative measure of coherence of the geoiogicai data.
  • the sembiance anaiyzer 104 computes local sembiance images using local smoothing filters. Local sembiance is a squared smoothed-image divided by a smoothed squa red-image, where smoothing is performed by local smoothing filters along the eigenvectors of a structure tensor.
  • the tensor field extractor 106 extracts a structure tensor,
  • the structure tensor is a structure tensor field, with a structure tensor associated wit each point in the geological data 102.
  • the structure tensor is extracted via a technique for extracting local structure in images which produces a tensor field, with a 2 x 2 tensor produced for each point in the input geological data 02.
  • the tensor field extractor 108 further extracts, for each point of the geological image, a smallest eigenvalue of the structure tensor.
  • the tensor field extractor 106 extracts, for each tensor in the structure tensor field, the ballness, the smallest eigenvalue of the structure tensor.
  • BaS!ness is a measure of orientation uncertainty or isotropy. In one example, it is indicative of points of low coherence, thereby supplementing the semblance analysis with additional evidence.
  • System 100 includes a phase congruency filter 108.
  • the semblance metric is passed through the phase congruency filter 108.
  • the phase congruency filter 108 extracts edges In images, and may be adjusted to extract longer edges while filtering out shorter edges. This may be utilized to emphasize long traces in the semblance metric while suppressing short traces.
  • the phase congruency filter 108 may be tuned to adjust parameters that are indicative of a length of a trace. Accordingly, traces that are longer than a threshold length may be extracted and retained, whereas traces that are shorter than the threshold length may be suppressed.
  • the phase congruency filter 108 provides a field of stick tensors 110, a 2 x 2 tensor produced for each point in the Input geological data 102, where each tensor has a single non-zero eigenvalue and carries both orientation and certainty of the edges detected by the phase congruency filter 108.
  • the evaiuator 112 determines, at each point of the geological image, a fault tensor based on the semblance metric, the structure tensor, and the stick tensor, the fault tensor indicative of a likelihood of each point lying along a potential fault in the geological image.
  • a magnitude of a product of the smallest eigenvalue of the structure tensor, the stick tensor, and the difference of the semblance metric from unity is determined.
  • the fault tensor may be determined as the following tensor field;
  • bailness x ( 1— semblance metric) x Stick Tensor where ballness is the smallest eigenvalue of the structure tensor extracted by the tensor field extractor 106, the semblance metric is generated by the semblance analyzer 104, and the stick tensor 110 is provided by the phase congruency filter 108.
  • the result is a field of sparse stick tensors that have a high likelihood of lying along a potential fault.
  • evaiuator 112 further interpolates the fault tensor to generate a continuous trace indicative of the potential fault in the geological image.
  • system 100 includes tensor voting which interpolates the fault tensor determined by the evaiuator 112, to create Song, smooth traces that follow Gestait principles of good continuation. The tensor voting fills in discontinuities in the fault tensor based on an assumption of continuity of a geological fault line.
  • the evaSuator further sharpens the continuous trace.
  • system 100 includes non-maximum suppression to sharpen the fault tensor.
  • the output of tensor voting may be a blurred image.
  • Non- maximum suppression provides a sparser, but contour-continuous fault tensor.
  • the evaiuator 112 further determines a magnitude of the fault tensor at each point of the geological image. For example, the largest eigenvalue of the fault tensor may be computed for each point of the geological data.
  • system 00 may include a highlighter to superimpose the potential fauit in the geological image onto the input geological image, in one example, an enhanced fault tensor may be generated based on tensor voting and/or non-maximum suppression, and the enhanced fault tensor may be superimposed on an input geological image to generate an output Image.
  • an enhanced fault tensor may be generated based on tensor voting and/or non-maximum suppression, and the enhanced fault tensor may be superimposed on an input geological image to generate an output Image.
  • the potential geological faults in the input geological image are highlighted. Accordingly, the noise typical of a semblance analysis is filtered out and the potential geological fault is highlighted.
  • Figure 2 is another functional block diagram illustrating one example of a system for finding faults in geological data.
  • Input geological data 202 is received.
  • Semblance analysts 204 is performed on the input geological data 202 to generate a semblance metric at each point of the geological data 202, trie sembiance metric indiicattve of iocai sembiance analysis.
  • a structure tensor 208 is extracted at each point of the geologicai data 202, the structure tensor 208 indicative of iocai structure in the geologicai data 202.
  • the semblance metric is filtered via a phase congruency 208 to provide a stick tensor at each point of the geological data 202, the stick tensor based on the semblance metric, and indicative of a point lying on a major edge represented by the geologicai data 202.
  • the sembiance metric, the stick tensor, and the structure tensor are combined to determine a fault tensor 210.
  • a magnitude of a product of the smallest eigenvalue of the structure tensor, the stick tensor, and the difference of the semblance metric from unity is determined.
  • the fault tensor 210 is further refined by applying tensor voting 212, in one example, the tensor resulting from the tensor voting 212 is furthe refined by applying non-maximum suppression 214, In one example, the fault tensor 210 is further refined by applying the non-maximum suppression 214, In one example, a magnitude of the fault tensor 210 is computed to provide an enhanced fault tensor 216. for example, the largest eigenvalue of the fault tensor may be computed for each point of the geological data 202, In one example, the enhanced fault tensor 218 is based on th tensor resulting from the tensor voting 212. In one example, the enhanced fault tensor 216 is based on the tensor resulting from the non-maximum suppression 214,
  • Figure 3 illustrates one example for finding faults in geologicai data
  • input geological image 302 is received.
  • Semblance analysis 304 is performed on the input geologicai image 302 to generate a semblance metric at each point of the geological image 302, the semblance metric indicative of Iocai semblance analysis 304.
  • a structure tensor is extracted at each point of the geological image 302, the structure tensor indicative of local structure in the geological image 302, In one example, the structure tensor is the ballness 306, the smallest eigenvalu of the structure tensor 306.
  • the semblance metric from semblance analysis 304 and the bailness 306 are combined to provide the fault tensor.
  • a magnitude 308 of the fault tensor is computed.
  • an enhanced fault tensor 310 is based on the tensor resulting from applying tensor voting to the fault tensor, in one example, the enhanced fault tensor 310 is
  • a first fault line 314 and a second fault line 316 may be
  • FIG. 4 is a block diagram illustrating one example of a processing system 400 for implementing the system 100 for finding faults in geological data.
  • Processing system 400 includes a processor 402, a memory 404, input devices 416, and output devices 418.
  • Processor 402, memory 404, input devices 416, and output devices 418 are coupled to each other through communication link ⁇ e.g., a bus).
  • i&02$3 Processor 402 includes a Central Processing Unit (CPU) or another suitable processor, in one example, memory 404 stores machine readable instructions executed by processor 402 for operating processing system 400.
  • Memory 404 includes any suitable combination of volatile and/or non-volatile memory, such as combinations of Random Access Memory (RAM), Read-Only Memory (ROM), flash memory, and/or other suitable memory.
  • Memory 404 stores input geological data 406 for processing by processing system 400.
  • memor 404 stores a seistrsoiogicaS image for processing by processing system 400.
  • memory 404 stores a three-dimensional image for processing by processing system 400.
  • Memory 404 also stores instructions to be executed by processor 402 including instructions for a semblance analyzer 408, a tensor field extractor 410, a phase congruency filter 412, and an evaluate* 414.
  • semblance analyzer 408, tensor field extractor 4 0, phase congruency filter 412, and evaiuator 414 include semblance analyzer 104, tensor field extractor 106, phase congruenc filter 108, and ⁇ valuator 1 12, respectively, as previously described and illustrated with reference to Figure 1.
  • memory 404 stores semblance metric, structure tensor, stick tensor, and fault tensor for processing by processing system 400, ⁇ 0030 ⁇ in one example, processor 402 utilizes the input geologica!
  • Processor 402 also utilizes the input geological data 408 and executes instructions of tensor field extractor 410 to extract a structure tensor at each point of the geological image, the structure tensor being indicative of local structure in the geological image.
  • the processor 402 utilizes the semblance metric and executes instructions of phase congruency filter 412 to provide a stick tensor at each point of the geological image, the stick tensor being indicative of a point lying on a major edge in the geoiogicai image, !n one example, processor 402 executes instructions of joint edge-aware filter 412 to provide piecewise smoothing of the input data based on a spatial measure of each data element in the input data, where the spatial measure is indicative of a maximal spatial extent of the piecewise smoothing at each data element.
  • processor 402 executes instructions of evaiuator 414 to determine, at each point of the geological image, a fault tensor based on the semblance metric, the structure tensor, and the stick tensor, the fault tensor being indicative of a likelihood of each point lying along a potential fauit in the geological image.
  • 10032 ⁇ input devices 416 include a keyboard, mouse, data ports, and/or other suitable devices for inputting information into processing system 400.
  • input devices 416 are used to input geological data 406, such as a geological image.
  • Output devices 418 include a monitor, speakers, data ports, and/or other suitable devices for outputting information from processing system 400.
  • an enhanced fault tensor may be generated based on tensor voting and/or non-maximum suppression, and the enhanced fault tenso may be superimposed on the input geological image to generate an output image.
  • Output devices 418 are used to output the output image with the potential geoiogicai fault highlighted.
  • FIG. 6 is a block diagram; illustrating one example of a computer readable medium for finding faults in geological data.
  • Processing system 500 includes a processor 502, a computer readable medium 510, a semblance analyzer 504, a tensor field extractor 508 s and a phase congruency filter 508.
  • Processor 502, computer readable medium 510, the semblance analyzer 504, the tensor field extractor 506, and the phase congruency filter 508 are coupled to each other through communication link (e.g., a bus).
  • communication link e.g., a bus
  • Processor 502 executes instructions included in the computer readable medium 510
  • Computer readable medium 510 includes geological data receipt instructions 512 to receive input geoiogicaf image.
  • Computer readable medium 510 includes semblance metric generation instructions 514 of the semblance analyzer 504 to generate a semblance metric at each point of the geological image, the semblance metric being indicative of local semblance analysis.
  • Computer readable medium 510 Includes structure tensor extraction instructions 518 of the tensor field extractor 506 to extract a structure tensor at each point of the geological image, the structure tensor being indicative of local structure in the geological image.
  • Computer readable medium 510 includes stick tensor provision instructions 518 of the phase congruency filter 508 to provide a stick tensor at each point of the geologica! image, the stick tensor being based on the semblance metric, and being indicative of a point lying on a major edge in the geological image.
  • Computer readable medium 510 includes fault tensor evaluation instructions 520 to evaluate, at each point of the geological image, a fault tensor based on the semblance metric, the structure tensor, and the stick tensor, the fault tensor being indicative of a likelihood of each point lying along a potential fault in the geological image.
  • Computer readable medium 5 0 includes fault superimposiiion instructions 522 to superimpose the potential fault In the geological image onto the input geological image,
  • computer readable medium 510 includes further instructions to evaluate, at each point, a magnitude of a product of a smallest eigenvalue of the structure tensor, the stick tensor, and the difference of the semblance metric from unity.
  • FIG. 8 is a Fiow diagram illustrating one example of a method for finding faults in geological data.
  • a geological image is received.
  • a semblance metric is generated at each point of the geological image.
  • a structure tensor is extracted at each point of the geological image.
  • a stick tensor is provided at each point of the geological image, the stick tensor being based on the semblance metric.
  • a fault tensor is evaluated based on the semblance metric, the structure tensor, and the stick tensor.
  • the fault tensor in the geological image is superimposed onto the input geological image.
  • the method includes providing the input geological image with the superimposed potentiai fault.
  • extracting the structure tensor includes extracting, for each point of the geological image, a smallest eigenvalue of the structure tensor.
  • evaluating the fault tensor includes evaluating a magnitude of a product of the smallest eigenvalue of the structure tensor, the stick tensor, and the difference of the semblance metric from unity.
  • evaluating the fault tensor includes interpolating the fault tensor to generate a continuous trace indicative of the potentiai fault in the geological image. In one example, evaluating the fault tensor Includes sharpening the continuous trace.
  • evaluating the fault tensor includes determining a magnitude of the fault tensor at each point of the geological image.
  • Examples of the disclosure provide a generalized system for finding faults in geologicai data based on semblance analysis enhanced with a filtering step to identify data related to a potential geologicai fault.
  • the generalized system provides a filter-based, automatable approach to finding potential fault in geological data by processing large geological datasets and looking for patterns that have a high likelihood of being potentiai faults, and filtering out other patterns.
  • the generalized system extracts long, potential fault structures while suppressing short structures.
  • the disclosed processes are parallelizable to run on multicore platforms, such as graphics processing units ("GPUs").
  • the disclosed processes are applicable to 3-D datasets.

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  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Acoustics & Sound (AREA)
  • Environmental & Geological Engineering (AREA)
  • Geology (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • General Physics & Mathematics (AREA)
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  • Geophysics And Detection Of Objects (AREA)

Abstract

L'invention porte sur la découverte de défauts dans des données géologiques. Un exemple est un système dans lequel une image géologique d'entrée est reçue. Un analyseur d'apparence génère une mesure d'apparence en chaque point de l'image, la mesure d'apparence étant indicative d'une analyse d'apparence locale. Un extracteur de champ de tenseurs extrait un tenseur de structure en chaque point de l'image, le tenseur de structure étant indicatif d'une structure locale dans l'image. Un filtre de congruence de phase délivre un tenseur linéaire en chaque point de l'image, le tenseur linéaire étant basé sur la mesure d'apparence, et étant indicatif d'un point se trouvant sur un bord principal dans l'image. Un évaluateur évalue, en chaque point de l'image, un tenseur de défaut basé sur la mesure d'apparence, le tenseur de structure et le tenseur linéaire, le tenseur de défaut étant indicatif d'une probabilité pour chaque point de se trouver le long d'un défaut potentiel dans l'image.
PCT/US2014/044632 2014-06-27 2014-06-27 Découverte de défauts dans des données géologiques WO2015199727A1 (fr)

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PCT/US2014/044632 WO2015199727A1 (fr) 2014-06-27 2014-06-27 Découverte de défauts dans des données géologiques

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PCT/US2014/044632 WO2015199727A1 (fr) 2014-06-27 2014-06-27 Découverte de défauts dans des données géologiques

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107621655A (zh) * 2017-08-29 2018-01-23 电子科技大学 基于DoS滤波的三维数据断层增强方法
CN110660051A (zh) * 2019-09-20 2020-01-07 西南石油大学 一种基于导航金字塔的张量投票处理方法

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EP0796442B1 (fr) * 1995-10-06 2001-12-12 Core Laboratories Global N.V. Procede et dispositif de prospection sismique et de traitement des signaux sismiques
US20100131205A1 (en) * 2007-03-12 2010-05-27 Geomage (2003) Ltd Method for identifying and analyzing faults/fractures using reflected and diffracted waves
US20110205844A1 (en) * 2010-02-22 2011-08-25 Landmark Graphics Corporation, A Haliburton Company Systems and Methods for Modeling 3D Geological Structures
EP2497900A2 (fr) * 2011-03-07 2012-09-12 Schlumberger Technology B.V. Modélisation des fractures hydrauliques
US20140163943A1 (en) * 2012-12-06 2014-06-12 Roxar Software Solutions As System for modeling geologic structures

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0796442B1 (fr) * 1995-10-06 2001-12-12 Core Laboratories Global N.V. Procede et dispositif de prospection sismique et de traitement des signaux sismiques
US20100131205A1 (en) * 2007-03-12 2010-05-27 Geomage (2003) Ltd Method for identifying and analyzing faults/fractures using reflected and diffracted waves
US20110205844A1 (en) * 2010-02-22 2011-08-25 Landmark Graphics Corporation, A Haliburton Company Systems and Methods for Modeling 3D Geological Structures
EP2497900A2 (fr) * 2011-03-07 2012-09-12 Schlumberger Technology B.V. Modélisation des fractures hydrauliques
US20140163943A1 (en) * 2012-12-06 2014-06-12 Roxar Software Solutions As System for modeling geologic structures

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107621655A (zh) * 2017-08-29 2018-01-23 电子科技大学 基于DoS滤波的三维数据断层增强方法
CN107621655B (zh) * 2017-08-29 2020-06-02 电子科技大学 基于DoS滤波的三维数据断层增强方法
CN110660051A (zh) * 2019-09-20 2020-01-07 西南石油大学 一种基于导航金字塔的张量投票处理方法
CN110660051B (zh) * 2019-09-20 2022-03-15 西南石油大学 一种基于导航金字塔的张量投票处理方法

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