Schlumberger Technology Corporation

Ihs Global Inc.

Ihs Inc.

Services Petroliers Schlumberger

Geoquest Systems B.v.

Yingwei Yu

Richard John Meehan

Velizar Vesselinov

Qiuhua Liu

Sylvain Chambon

Yuelin Shen

Cheolkyun Jeong

Wei Chen

Clifford Lee Kelley

Irina M Mardanova

Mohammad Khairi Hamzah

Minh Trang Chau

Fatma Mahfoudh

Nathaniel Wicks

Crispin Chatar

Zhengxin Zhang

James P Belaskie

Aymeric Lilian Jan

Sai Venkatakrishnan

Darine Mansour

Sergey Mikhailovich Makarychev-Mikhailov

Priya Mishra

Douglas J Seyler

Gordana Draskovic

Michael D McCormack

Gregory Michael Skoff

Georgia Kouyialis

Yezid Arevalo Romero

Oleh Petryshak

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

The present disclosure relates to systems, non-transitory computer-readable media, and methods for detecting a washout or other anomaly event in a wellbore. In particular, in one or more embodiments, the disclosed systems receive a plurality of measurements including a measured flow rate into the wellbore, a measured weight on a drill bit in the wellbore, a measured depth of the drill bit in the wellbore, and a measured pressure at a standpipe of the wellbore. In one or more embodiments, the disclosed systems estimate one or more parameters of a physical model for determining a theoretical estimate of the standpipe pressure. In one or more embodiments, the disclosed systems determine a probability that the washout or other anomaly event is occurring in the wellbore based at least partially upon the measurements and the theoretical estimate of the standpipe pressure.

Hybrid neural network for drilling anomaly detection

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 method can include receiving sensor data; determining a rate of penetration drilling parameter value using a trained neural network and at least a portion of the sensor data; and issuing a control instruction for drilling a borehole using the determined rate of penetration drilling parameter value.

Drilling control

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.

A system and method that can include training a deep neural network using time series data that represents functions of a non-linear Kalman filter that represents a dynamic system of equipment and environment and models a pre-defined operational procedure as a temporal sequence. The system and method can also include receiving operation data from the equipment responsive to operation in the environment and outputting an actual operation as an actual sequence of operational actions by the deep neural network. The system and method can additionally include performing an operation-level comparison to evaluate the temporal sequence against the actual sequence using a distance function in a latent space of the deep neural network and outputting a score function that quantifies the distance function in the latent space. The system and method can further include controlling an electronic component to execute an electronic operation based on the score function.

Field operations system with filter

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 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 can include training a deep neural network to generate a trained deep neural network where the trained deep neural network represents functions of a nonlinear Kalman filter that represents a dynamic system of equipment and environment via an internal state vector of the dynamic system; generating a base internal state vector, that corresponds to a pre-defined operational procedure, using the trained deep neural network; receiving operation data from the equipment responsive to operation in the environment; generating an internal state vector using the operation data and the trained deep neural network; and comparing at least the internal state vector to at least the base internal state vector.

A method can include receiving multi-channel time series data of drilling operations; training a deep neural network (DNN) using the multi-channel time series data to generate a trained deep neural network as part of a computational simulator where the deep neural network includes at least one recurrent unit; simulating a drilling operation using the computational simulator to generate a simulation result; and rendering the simulation result to a display.

Field operations system

A method can include receiving channels of data from equipment responsive to operation of the equipment in an environment where the equipment and environment form a dynamic system; defining a particle filter that localizes a time window with respect to the channels of data; applying the particle filter at least in part by weighting particles of the particle filter using the channels of data, where each of the particles represents a corresponding time window; and selecting one of the particles according to its weight as being the time window of an operational state of the dynamic system.

Field operations system with particle filter

A method includes acquiring data associated with a field operation of equipment in a geologic environment; filtering the data using a filter where the filter includes, along a dimension, a single maximum positive value that decreases to a single minimum negative value that increases to approximately zero; and, based on the filtering, issuing a control signal to the equipment in the geologic environment.

Dynamic field operations system

A method includes acquiring data during rig operations where the rig operations include operations that utilize a bit to drill rock and where the data include different types of data; analyzing the data utilizing a probabilistic mixture model for modes, a detection engine for trends and a network model for an inference based at least in part on at least one of a mode and a trend; and outputting information as to the inference where the inference characterizes a relationship between the bit and the rock.

Real-time well construction process inference through probabilistic data fusion

A symmetry attribute is described that may be used for determining seismic stratigraphic features in a formation. In one example, seismic input data from a formation is processed to determine an attribute by selecting a center trace, assigning a first cluster of the traces to a left image and a second cluster of the traces to a right image, and determining symmetry about the center trace between the left and the right images.

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Katy, TX, United States of America

Yingwei Yu