公开(公告)号：US12287442B2

公开(公告)日：2025-04-29

申请号：US17738247

申请日：2022-05-06

Applicant: Landmark Graphics Corporation

Inventor： Xuan Nam Nguyen ,  Sinan Tufekci ,  Alejandro Jaramillo

IPC: G01V1/30 ,  E21B44/00 ,  E21B49/00 ,  G06F17/18 ,  G06F30/20

Abstract: The disclosure presents processes to automatically generate one or more set of fault segments from a fault plane pointset. The processes can identify a predominant direction and derive a set of fault segments from the fault plane pointset, where the fault segments are generated by using slices of data from the fault plane pointset that are perpendicular to the predominant direction. For each slice of data, the fault segments can be analyzed with neighboring fault segments to determine if they are overlapping. Fault segments that block or overlap other fault segments can be assigned to a different subset of fault segments from the underlying fault segments. Gaps in the fault plane pointset, and the resulting set of fault segments, can be filled in by merging neighboring fault segments above and below the gap if the neighboring fault segments satisfy a criteria for filling the gap.

公开(公告)号：US12241366B2

公开(公告)日：2025-03-04

申请号：US17553738

申请日：2021-12-16

Applicant: Landmark Graphics Corporation

Inventor： Swaminathan Kiran Kumar ,  Robello Samuel

IPC: E21B47/12 ,  E21B45/00

Abstract: Drilling parameters for a wellbore operation can be determined based on resonance speeds. For example, a system can receive real-time data for a drilling operation that is concurrently occurring with receiving the real-time data. The system can determine, for a drilling depth, a rotations-per-minute (RPM) value corresponding to a resonance speed based on a weight-on-bit (WOB) value and the real-time data. The system can generate a plot of the WOB value and the RPM value corresponding to the resonance speed. The system can determine drilling parameters for the drilling operation based on the plot. The drilling parameters can exclude, for the WOB value, the RPM value corresponding to the resonance speed.

公开(公告)号：US20250061255A1

公开(公告)日：2025-02-20

申请号：US18759280

申请日：2024-06-28

Applicant: Landmark Graphics Corporation

Inventor： Benjamin Freestone

IPC: G06F30/28 ,  E21B49/00

Abstract: A method for analyzing an unproduced reservoir that includes obtaining a new reservoir dataset, for the unproduced reservoir, that includes a plurality of desired reservoir data types, identifying a plurality of existing reservoir datasets, in a historical reservoir database, where each reservoir dataset, of the plurality of existing reservoir datasets, includes a desired reservoir data type of the plurality of desired reservoir data types, training a plurality of finalist machine learning models using the plurality of existing reservoir datasets, identifying a best finalist machine learning model of the plurality of finalist machine learning models, and processing the new reservoir dataset, using best finalist machine learning model, to generate analysis data for the unproduced reservoir.

公开(公告)号：US20240192400A1

公开(公告)日：2024-06-13

申请号：US18079655

申请日：2022-12-12

Applicant: Landmark Graphics Corporation

Inventor： Genbao Shi ,  Raquel Medina ,  Sebastien Bruno Strebelle

IPC: G01V99/00 ,  E21B49/00

CPC classification number: G01V99/005 ,  E21B49/00 ,  E21B2200/20

Abstract: In some embodiments, a method for computing, by a volume data processor, volumetrics of a subsurface region without gridlines associated with the subsurface region comprises creating, in the volume data processor, a geometry representing the subsurface region and first bounding box about the geometry, computing a first probability that a group of sampled points inside the first bounding box are inside the geometry, and computing a gross rock volume (GRV) of the geometry by multiplying the first probability by a volume of the first bounding box.

公开(公告)号：US12000976B2

公开(公告)日：2024-06-04

申请号：US17006110

申请日：2020-08-28

Applicant: Landmark Graphics Corporation

Inventor： Sebastian Kroczka ,  Welton Danniel Souza ,  Chafaa Badis

IPC: G01V20/00 ,  E21B47/085 ,  G01V1/40 ,  G06F30/27 ,  G06F113/08 ,  G06F113/14 ,  G06N20/00

CPC classification number: G01V20/00 ,  E21B47/085 ,  G01V1/40 ,  G06F30/27 ,  G06N20/00 ,  E21B2200/20 ,  G06F2113/08 ,  G06F2113/14

Abstract: A method for training a well model to predict material loss for a pipe string having a wall thickness and located within a borehole. The method may include measuring the wall thickness of a first pipe string at locations axially along the first pipe string with a logging tool at a first time. The method may also include measuring the wall thickness of the first pipe string at the locations with the logging tool at a second time. The method may further include training a first well model based on a machine learning (“ML”) algorithm to predict a predicted amount of material loss in the future for the first pipe string at a selected location using the wall thickness measurements at the first and second times and well operating condition information related to the first pipe string.

公开(公告)号：US11982158B2

公开(公告)日：2024-05-14

申请号：US17258539

申请日：2020-03-24

Applicant: Landmark Graphics Corporation

Inventor： Zhengchun Liu ,  Robello Samuel ,  Adolfo Gonzales ,  Yongfeng Kang

IPC: G06F30/17 ,  E21B41/00 ,  E21B17/00

CPC classification number: E21B41/00 ,  G06F30/17 ,  E21B17/00

Abstract: A method for designing a borehole tubular for use in a borehole. The method may include defining tubular sections that make up the borehole tubular, defining a downhole operation that will be conducted using the borehole tubular at a first timestamp, determining loads that will be applied to each of the tubular sections at respective specific depths along the borehole during the downhole operation at the first timestamp, determining a design limit envelope for each of the tubular sections at the first timestamp based on design parameters of the tubular section and the specific depth of the tubular section at the first timestamp, and displaying a three-dimensional (3D) plot of the design limit envelopes of the tubular sections and the loads applied to the tubular sections as a function of depth within the borehole on a display.

公开(公告)号：US11977196B2

公开(公告)日：2024-05-07

申请号：US16945222

申请日：2020-07-31

Applicant: Landmark Graphics Corporation

Inventor： Jocelyn Chan ,  Nam Xuan Nguyen ,  Kainan Wang ,  Xuewei Tan

IPC: G01V1/30

CPC classification number: G01V1/301 ,  G01V2210/643

Abstract: Systems and methods for automatically tracking multi-Z horizons within seismic volumes are provided. A surface from a plurality of surfaces identified at different depths for a multi-Z horizon within a seismic volume is selected. A seed point corresponding to the selected surface is determined. The selected surface is tracked over new data points through the seismic volume. Tracking each new data point involves comparing a depth of the new data point with depths associated with other surfaces and determining whether the depth of the new data point honors a geological boundary rule for maintaining a relative depth position of each of the plurality of surfaces within the multi-Z horizon, based on the comparison. When the depth of the new data point honors the rule, the selected surface is extended to include the new data point and, when it does not, the new data point is discarded.

公开(公告)号：US11965997B2

公开(公告)日：2024-04-23

申请号：US17505033

申请日：2021-10-19

Applicant: Landmark Graphics Corporation

Inventor： Xuan Nam Nguyen ,  Alejandro Jaramillo

IPC: G01V1/30

CPC classification number: G01V1/301 ,  G01V1/306 ,  G01V1/302 ,  G01V2210/65

Abstract: Hydrocarbon exploration and extraction can be facilitated by determining fault surfaces from fault attribute volumes. For example, a system described herein can receive a fault attribute volume for faults in a subterranean formation determined using seismic data. The fault attribute volume may include multiple traces with trace locations. The system can determine a set of fault samples for each trace location. Each fault sample can include fault attributes such as a depth value, an amplitude value, and a vertical thickness value. The system can determine additional fault attributes such as a dip value and an azimuth value for each fault sample of each trace location. The system can determine fault surfaces for the faults using the fault samples and fault attributes. The system can then output the fault surfaces for use in a hydrocarbon extraction operation.

公开(公告)号：US11959374B2

公开(公告)日：2024-04-16

申请号：US17256164

申请日：2020-02-03

Applicant: LANDMARK GRAPHICS CORPORATION

Inventor： Mahdi Parak ,  Srinath Madasu ,  Egidio Marotta ,  Dale McMullin ,  Nishant Raizada

IPC: E21B44/02 ,  G05B13/02 ,  G05B13/04 ,  G06N20/00

CPC classification number: E21B44/02 ,  G05B13/0265 ,  G05B13/042 ,  G05B13/048 ,  G06N20/00 ,  E21B2200/20 ,  E21B2200/22

Abstract: System and methods for event prediction during drilling operations are provided. Regression data associated with coefficients of a predictive model are retrieved for a downhole event during a drilling operation along a planned path of a wellbore. The regression data includes a record of changes in historical coefficient values associated with prior occurrences of the event. As the wellbore is drilled over different stages of the operation, a value of an operating variable is estimated based on values of the coefficients and real-time data acquired during each stage. A percentage change in coefficient values adjusted between successive stages of the operation is tracked. An occurrence of the downhole event is estimated, based on a correlation between the percentage change tracked for at least one coefficient and a corresponding change in the historical coefficient values. The path of the wellbore is adjusted, based on the estimated occurrence of the event.

公开(公告)号：US11954567B2

公开(公告)日：2024-04-09

申请号：US16963313

申请日：2020-02-20

Applicant: Landmark Graphics Corporation

Inventor： Jiazuo Zhang ,  Graham Baines

IPC: G06F18/22 ,  G01V20/00 ,  G06F18/214 ,  G06F18/2431 ,  G06N20/00

CPC classification number: G06N20/00 ,  G01V20/00 ,  G06F18/214 ,  G06F18/22 ,  G06F18/2431 ,  G01V2210/66

Abstract: According to some aspects, machine-learning models can be executed to classify a subsurface rock. Examples include training numerous machine-learning models using training data sets with different probability distributions, and then selecting a model to execute on a test data set. The selection of the model may be based on the similarity of each data point of the test data set and the probability distribution of each training class. Examples include detecting and recommending a pre-trained model to generate outputs predicting a classification, such as a lithology, of a test data set. Recommending the trained model may be based on calculated prior probabilities that measure the similarity between the training and test data sets. The model with a training data set that is most similar to the test data set can be recommended for classifying a physical property of the subsurface rock for hydrocarbon formation.