Schlumberger Technology Corporation

Sensia LLC

Services Petroliers Schlumberger

Geoquest Systems B.v.

Velizar Vesselinov

Yingwei Yu

Yuelin Shen

Richard John Meehan

Jose Celaya Galvan

Wei Chen

Minh Trang Chau

Emilien Dupont

Sergey Doronichev

Xiaotong Suo

Qiuhua Liu

Cheolkyun Jeong

Valerian Guillot

Carlos Boneti

Neil Holger White Eklund

Vladimir Zhernakov

Sylvain Chambon

Crispin Chatar

Andrey Sergeevich Konchenko

Zhengxin Zhang

Erik Burton

Sergey Mikhailovich Makarychev-Mikhailov

Fatma Mahfoudh

Yezid Arevalo Romero

Oleh Petryshak

Georgia Kouyialis

Adam David Sallee

Gregory Michael Skoff

Priya Mishra

Steven Hann

Oswaldo Bustos Angel

Philip Christopher Temple

Diego Fernando Patino Virano

Jose Ramon Celaya Galvan

The present disclosure relates to systems, methods, and non-transitory computer-readable media for dynamically utilizing offset drill-well data generated within a threshold geographic area to determine formation-top trends and identify formation-top depths at a subject drill-well site. To do so, in some embodiments, the disclosed systems estimate a variogram for observed formation-top depths of a subset of offset drill-wells, and, in turn, map a predicted response from the estimated variogram. For example, using weighted combinations (e.g., with Kriging weights) of the formation-top depths of the subset of offset drill-wells, the disclosed systems can map a continuous surface of a formation and identify a top-depth thereof. Moreover, the disclosed system can do so for multiple formations at the subject drill-well site, and (in real-time in response to a user input) provide for display at a client device, the associated formation-top depths, various predicted drilling events and/or predicted drilling metrics.

Predicting formation-top depths and drilling performance or drilling events at a subject location

A method can include receiving input for a drilling operation that utilizes a bottom hole assembly and drilling fluid; generating a set of offset drilling operations using historical feature data, where the historical feature data are processed by computing feature distances; performing an assessment of the offset drilling operations as characterized by at least feature distance-based similarity between the drilling operation and the offset drilling operations; and outputting at least one recommendation for selection of one or more of a component of the bottom hole assembly and the drilling fluid based on the assessment.

Well construction equipment framework

A system and method that include receiving offset well data collected from an offset well, wherein the offset well data comprises data representing a trajectory of an offset. The system and method also include receiving subject well data comprising a trajectory of at least a portion of a subject. The system and method additionally include determining a similarity value between the trajectory of the offset well and the subject well. The system and method also include selecting at least one offset well for offset well analysis based on the similarity value. The system and method further include adjusting at least one parameter of the subject well based on the offset well analysis.

Offset well analysis using well trajectory similarity

A system and method that include receiving sensor data during drilling of a portion of a borehole in a geologic environment. The system and method also include selecting a drilling mode from a plurality of drilling modes based at least on a portion of the sensor data. The system and method additionally include simulating drilling of the borehole using the selected drilling mode and generating a state of the borehole in the geologic environment based on the simulated drilling of the borehole. The system and method further include generating a reward using the state of the borehole and a planned borehole trajectory and using the reward through deep reinforcement learning to maximize future rewards for drilling actions.

Drilling control

A system can include a processor; memory operatively coupled to the processor; and processor-executable instructions stored in the memory to instruct the system to: receive a marker on a well log for a well in a geographic region; and iteratively propagate the marker automatically to a plurality of well logs for other wells in the geographic region.

Well log correlation and propagation system

A method for drilling a well includes generating a plurality of proposed drilling actions using a plurality of working agents based on a working environment, simulating drilling responses to the proposed drilling actions using a plurality of validation agents in a validation environment that initially represents the working environment, determining rewards for the proposed drilling actions based on the simulating, using the validation agents, selecting one of the proposed drilling actions, and causing a drilling rig to execute the selected one of the proposed actions.

Multi-agent drilling decision system and method

A method for offset well analysis includes receiving offset well data collected from an offset well, the offset well data including data representing a trajectory of an offset well, receiving subject well data comprising a trajectory of at least a portion of a subject well, partitioning the trajectory of the offset well into a plurality of offset well segments, partitioning the trajectory of the subject well into a plurality of subject well segments, determining a distance between at least some of the plurality of offset well segments and at least some of the plurality of subject well segments, selecting the offset well based in part on the distance, and performing an offset well analysis using the offset well and the subject well.

A method can include receiving sensor data during drilling of a portion of a borehole in a geologic environment; determining a drilling mode from a plurality of drilling modes using a trained neural network and at least a portion of the sensor data; and issuing a control instruction for drilling an additional portion of the borehole using the determined drilling mode.

Methods, computing systems, and computer-readable media for interpreting drilling dynamics data are described herein. The method can include receiving drilling dynamics data simulated by a processor or collected by a sensor positioned in a drilling tool. The method can further include extracting a feature map from the drilling dynamics data, and determining that a feature zone from the feature map corresponds to a predetermined dynamic state. The feature zone can be determined using a neural network trained to associate feature zones with dynamic states. Additionally, the method can include selecting a drilling parameter for a drill string based on the feature zone.

Automatic interpretation of drilling dynamics data

A method may include obtaining, by the computer processor, rig operation data regarding various drilling rigs at different geographic locations. The method may further include generating, using the rig operation data, a model that identifies a level of risk associated with various rig operations. The method may further include simulating, by the computer processor and using the geographic location data and the model, a sequence of rig operations for constructing a portion of a wellbore drilled by a drilling rig at the desired geographic location.

Method and system for integrated well construction

Computing system assessment of geological similarity of wells employing well-log data includes receiving logs with chronostratigraphic markers, and extracting signatures from the logs using the chronostratigraphic markers. A distance matrix is generated from the signatures using dynamic time warping. Using the distance matrix, a set of clusters are generated. The set of clusters are presented with an image.

Computing system assessment of geological similarity of wells employing well-log data

A method includes receiving data where the data include data for a plurality of factors associated with a plurality of wells; training a regression model based at least in part on the data and the plurality of factors; outputting a trained regression model; and predicting production of a well via the trained regression model.

Resource production forecasting

Growing community of inventors

Katy, TX, United States of America

Velizar Vesselinov