# US 11,307,319 B2 · Automated fault uncertainty analysis in hydrocarbon exploration > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,307,319 B2 - Document ID: US11307319B2 - Status: Granted - Kind: B2 - Application number: 16/929,911 - Filed: 2020-07-15 - Published: 2022-01-20 - Granted: 2022-04-19 - Earliest claimed: 2020-07-15 - Assignees: Landmark Graphics Corporation - Applicants: Landmark Graphics Corporation - Inventors: Andreas Rueger; Steven Roy Angelovich - Web page: https://patentgrep.com/patent/US11307319B2 ## CPC - G01V 1/301: {for determining seismic cross-sections or geostructures} - G01V 1/282: {Application of seismic models, synthetic seismograms} - G01V 1/307: {for determining seismic attributes, e.g. amplitude, instantaneous phase or frequency, reflection strength or polarity} - G01V 1/345: {Visualisation of seismic data or attributes, e.g. in 3D cubes} - G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting - G06N 3/02: Neural networks - G06N 3/0499: Feedforward networks - G06N 3/08: Learning methods - G06N 3/09: Supervised learning - G06V 10/761: {Proximity, similarity or dissimilarity measures} - G06V 20/10: Terrestrial scenes (scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59) - G01V 2210/642: Faults - G01V 2210/667: Determining confidence or uncertainty in parameters - G01V 2210/74: Visualisation of seismic data - G01V: GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS (means for indicating the location of accidentally buried, e.g. snow-buried, persons A63B29/02) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract A system includes a processor and a memory. The memory includes instructions that are executable by the processor to access a plurality of seismic images of a subterranean formation in a first geological area. The instructions are also executable to generate a plurality of fault estimates from each of the plurality of seismic images. Further, the instructions are executable to generate a processed seismic image of the first geological area by normalizing and merging the plurality of seismic images and the plurality of fault estimates. Additionally, the instructions are executable to generate a statistical fault uncertainty volume of the first geological area using the processed seismic image. Furthermore, the instructions are executable to control a drilling operation in the first geological area using the statistical fault uncertainty volume of the first geological area. ## Claims ### Claim 1 A system comprising: a processor; and a memory including instructions that are executable by the processor for causing the processor to: access a plurality of seismic images of a subterranean formation in a first geological area; generate a plurality of fault estimates from each of the plurality of seismic images; generate a processed seismic image of the first geological area by normalizing and merging the plurality of seismic images and the plurality of fault estimates; generate a statistical fault uncertainty volume of the first geological area using the processed seismic image; and control a drilling operation in the first geological area using the statistical fault uncertainty volume of the first geological area. ### Claim 2 (depends on 1) The system of claim 1, wherein the instructions are further executable by the processor for causing the processor to: generate a prediction model by training a deep neural network using the statistical fault uncertainty volume of the first geological area and a first set of seismic attributes of the first geological area; and generate a confidence index of a fault location in a second geological area by applying the prediction model to a second set of seismic attributes of the second geological area. ### Claim 3 (depends on 2) The system of claim 2, wherein the instructions are further executable by the processor for causing the processor to: control a drilling operation in the second geological area using the confidence index of the fault location in the second geological area. ### Claim 4 (depends on 1) The system of claim 1, wherein the plurality of seismic images comprise at least three different seismic images. ### Claim 5 (depends on 4) The system of claim 4, wherein the plurality of seismic images are generated from a plurality of sub-angle ranges used to receive seismic wave reflections from the subterranean formation in the first geological area. ### Claim 6 (depends on 1) The system of claim 1, wherein controlling the drilling operation in the first geological area comprises controlling the drilling operation to avoid a portion of the first geological area where the statistical fault uncertainty volume indicates a high degree of uncertainty in a fault-presence prediction. ### Claim 7 (depends on 1) The system of claim 1, wherein controlling the drilling operation in the first geological area is performed prior to drilling into a fault in the first geological area. ### Claim 8 (depends on 1) The system of claim 1, wherein the statistical fault uncertainty volume comprises an indication of uncertainty that a particular pixel associated with a location in a subterranean formation is along a fault. ### Claim 9 A method comprising: accessing, by a processor, a plurality of seismic images of a subterranean formation in a first geological area; generating, by the processor, a plurality of fault estimates from each of the plurality of seismic images; generating, by the processor, a processed seismic image of the first geological area by normalizing and merging the plurality of seismic images and the plurality of fault estimates; generating, by the processor, a statistical fault uncertainty volume of the first geological area using the processed seismic image; generating, by the processor, a prediction model by training a machine-learning architecture using the statistical fault uncertainty volume of the first geological area and a first set of seismic attributes of the first geological area; and generating, by the processor, a confidence index of a fault location in a second geological area by applying the prediction model to a second set of seismic attributes of the second geological area. ### Claim 10 (depends on 9) The method of claim 9, further comprising: controlling, by the processor, a drilling operation in the second geological area using the confidence index of the fault location in the second geological area. ### Claim 11 (depends on 9) The method of claim 9, further comprising: controlling, by the processor, a drilling operation in the first geological area using the statistical fault uncertainty volume of the first geological area. ### Claim 12 (depends on 11) The method of claim 11, wherein controlling the drilling operation in the first geological area is performed prior to drilling into a fault in the first geological area. ### Claim 13 (depends on 9) The method of claim 9, wherein the statistical fault uncertainty volume comprises an indication of uncertainty that a particular pixel associated with a location in a subterranean formation is along a fault. ### Claim 14 (depends on 9) The method of claim 9, wherein generating the plurality of fault estimates from each of the plurality of seismic images comprises: processing, by the processor, the plurality of seismic images using a plurality of algorithmic hyper-parameter settings on each of the plurality of seismic images to generate the plurality of fault estimates. ### Claim 15 A non-transitory computer-readable medium comprising program code that is executable by a processor for causing the processor to: access a plurality of seismic images of a subterranean formation in a first geological area; generate a plurality of fault estimates from each of the plurality of seismic images; generate a processed seismic image of the first geological area by normalizing and merging the plurality of seismic images and the plurality of fault estimates; generate a statistical fault uncertainty volume of the first geological area using the processed seismic image; generate a prediction model by training a deep neural network using the statistical fault uncertainty volume of the first geological area and a first set of seismic attributes of the first geological area; and generate a confidence index of a fault location in a second geological area by applying the prediction model to a second set of seismic attributes of the second geological area. ### Claim 16 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the program code is further executable by the processor for causing the processor to: control a drilling operation in the second geological area using the confidence index of the fault location in the second geological area. ### Claim 17 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the program code is further executable by the processor for causing the processor to: control a drilling operation in the first geological area using the statistical fault uncertainty volume of the first geological area. ### Claim 18 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the statistical fault uncertainty volume comprises an indication of uncertainty that a particular pixel associated with a location in a subterranean formation is along a fault.