Landmark Graphics Corporation

Halliburton Energy Services, Inc.

Landmark Graphics Corporaton

Srinath Madasu

Keshava Prasad Rangarajan

Avi Lin

Shashi Dande

Joshua Lane Camp

Nishant Raizada

Robello Samuel

Vladimir Nikolayevich Martysevich

Yevgeniy Zagayevskiy

Tyler Austen Anderson

Raja Vikram R Pandya

Yashas Malur Saidutta

Ubong Akpan Inyang

Terry Wayne Wong

Jie Bai

Mahdi Parak

Yogendra Narayan Pandey

Egidio Marotta

Shohreh Amini

Azor Nwachukwu

Zhi Chai

Hongfei Wu

Keshava Rangarajan

Terry Wong

Philip D Nguyen

Ronald Glen Dusterhoft

Dipti Singh

Jeffrey Marc Yarus

Andrey V Filippov

Dinesh Ananda Shetty

Steven Ward

Aaron Gene Russell

Oluwatosin Ogundare

Dominic Camilleri

Jianxin Lu

Geoffrey Wedell Gullickson

Ashwani Dev

Satyam Priyadarshy

Joseph Blake Winston

Avinash Wesley

Youli Mao

Pandurang Manohar Kulkarni

Xi Wang

Yijie Shen

Samuel Bryant Johnson

Hanife Meftun Erdogan

Hui Dong

Raja Vikram Raj Pandya

Brice Y Kim

Xinli Jia

Hanzi Mao

Aimee Jackson Taylor

Bhaskar Mandapaka

Harsh Biren Vora

Charles Hai Wang

Courtney Leeann Beck

Yucel Akkutlu

Brian A Freno

Karan Dhuldhoya

ZhiXiang Jiang

Raja Vikram Pandya

Srinivasan Jagnnathan

Da Pang

Dale McMullin

Wei Chiu

Jeffery Padgett

Dale Mcmullin

Zhixiang Jiang

Geoffrey W Gullickson

System for optimizing operation of an oil and gas well employs multi-objective Bayesian optimization of wellbore parameters to minimize scaling and corrosion. The system may contain instrumentation for measuring temperature, pressure, at least one production parameter and at least one ion concentration of the fluid in the wellbore. The system may also have a processor for performing a calculation procedure to determine an anticipated corrosion rate ('Vbase') and a scaling index (“Is”) reflecting a tendency of scale to form in the wellbore based on the measurements provided by the instrumentation, where Vbase and Is are calculated along the length of the wellbore. Based on a selected set of optimization points taken from the calculations of Vbase and Is, the system may control the alkalinity and flow rate of the fluid based on the multi-objective optimization to simultaneously optimize scaling and corrosion.

Multi-objective optimization on modeling and optimizing scaling and corrosion in a wellbore

An apparatus for generating forecasts from a high-dimensional parameter data space comprising a reservoir model, a model order reduction module, and an assisted history matching module. The reservoir model having input variables, output variables, and an algorithmic model. The input variables, output variables, and the algorithmic model are generated by a flow simulator module and from a formation and reservoir properties database and a field production database. The model order reduction module generates a subset of the original or transformed input variables. The assisted history matching module adjust values of the output variables based on a difference between the at least one of the output variables and dynamic field production data to improve model accuracy.

Model parameter reductions and model parameter selection to optimize execution time of reservoir management workflows

An apparatus used to generate forecasts from a high-dimensional parameter data space. The apparatus comprising a reservoir model and a flow simulator module. The reservoir model comprising a plurality input variables, output variables, and at least one algorithmic model. The input variables and output variables are generated by the flow simulator module and variables from a formation and reservoir properties database and a field production database. The flow simulator module generates the at least one algorithmic model and the output variables using at least one selected from a group comprising a full-physics flow simulator, proxy flow simulator for assisted history matching, and a proxy flow simulator for field development optimization. The full-physics flow simulator and the two proxy flow simulators generate the at least one algorithmic model using at least one selected from a group comprising the reservoir model, history matching input variables, and optimization input variables.

Flow simulator for generating reservoir management workflows and forecasts based on analysis of high-dimensional parameter data space

A system, for controlling well site operations, comprising a machine learning engine, a predictive engine, a node system stack, and a blockchain. The learning engine includes a machine learning algorithm, an algorithmically generated earth model, and control variables. The learning algorithm generates a trained data model using the algorithmically generated earth model. The predictive engine includes an Artificial Intelligence (AI) algorithm. The AI algorithm generates a trained AI algorithm using the trained data model and earth model variables using the trained AI algorithm. The system stack is communicable coupled to the predictive engine, the learning engine, the blockchain, sensors, and a machine controller. The blockchain having a genesis block and a plurality of subsequent blocks. Each subsequent block comprising a well site entry and a hash of a previous entry. The well site entry comprises transacted operation control variables. The transacted variables are based on the generated earth model variables.

AI/ML, distributed computing, and blockchained based reservoir management platform

The disclosed embodiments include pump systems and methods to improve pump load predictions of pumps. The method includes determining, in a neural network, a pump load of a wellbore pump based on a physics based model of the pump load of the wellbore pump. The method also includes obtaining a measured pump load of the wellbore pump. After initiation of a pump cycle of the wellbore pump, the method further includes predicting a pump load of the wellbore pump based on the physics based model, performing a Bayesian Optimization to reduce a difference between a predicted pump load and the measured pump load to less than a threshold value, and improving a prediction of the pump load based on the Bayesian Optimization.

Pump systems and methods to improve pump load predictions

Geosteering can be used in a drilling operation to create a wellbore that is used to extract hydrocarbons from a defined zone within the subterranean formation. According to some aspects, generating paths for the wellbore may include using path-planning protocols and pure-pursuit protocols. The pure-pursuit protocol may be executed to output a plurality of candidate drilling paths. The output may also include control parameters for controlling the drill bit. A trajectory optimizer may determine a result of multi-objective functions for each candidate path. A cost function may represent a cost or loss associated with a candidate path. Additionally, the trajectory optimizer may perform an optimization protocol, such as Bayesian optimization, on the cost functions to determine which candidate path to select. The selected candidate path may correspond to new control parameters for controlling the drill bit to reach the target location.

Well path drilling trajectory and control for geosteering

A system and method for controlling multiple drilling tools inside wellbores makes use of Bayesian optimization with range constraints. A computing device samples observed values for controllable drilling parameters such as weight-on-bit, mud flow rate and drill bit rotational speed in RPM and evaluates a selected drilling parameter such a rate-of-penetration and hydraulic mechanical specific energy for the observed values using an objective function. Range constraints including the physical drilling environment and the total power available to all drilling tools within the drilling environment can be continuously learned by the computing device as the range constraints change. A Bayesian optimization, subject to the range constraints and the observed values, can produce an optimized value for the controllable drilling parameters to achieve a predicted value for the drilling parameters. The system can then control the drilling tool using the optimized value to achieve the predicted value for the selected drilling parameter.

Multi-well drilling optimization using robotics

System and methods for training neural network models for real-time flow simulations are provided. Input data is acquired. The input data includes values for a plurality of input parameters associated with a multiphase fluid flow. The multiphase fluid flow is simulated using a complex fluid dynamics (CFD) model, based on the acquired input data. The CFD model represents a three-dimensional (3D) domain for the simulation. An area of interest is selected within the 3D domain represented by the CFD model. A two-dimensional (2D) mesh of the selected area of interest is generated. The 2D mesh represents results of the simulation for the selected area of interest. A neural network is then trained based on the simulation results represented by the generated 2D mesh.

Effective representation of complex three-dimensional simulation results for real-time operations

Using production data and a production flow record based on the production data, a deep neural network (DNN) is trained to model a proxy flow simulation of a reservoir. The proxy flow simulation of the reservoir is performed, using an ensemble Kalman filter (EnKF), based on the trained DNN. The EnKF assimilates new data through updating a current ensemble to obtain history matching by minimizing a difference between a predicted production output from the proxy flow simulation and measured production data from a field. Using the updated current ensemble, a second proxy flow simulation of the reservoir is performed based on the trained DNN. The assimilating and the performing are repeated while new data is available for assimilating. Predicted behavior of the reservoir is determined based on the proxy flow simulation of the reservoir. An indication of the predicted behavior is provided to facilitate production of fluids from the reservoir.

Petroleum reservoir behavior prediction using a proxy flow model

Systems and methods for automated drilling control and optimization are disclosed. Training data, including values of drilling parameters, for a current stage of a drilling operation are acquired. A reinforcement learning model is trained to estimate values of the drilling parameters for a subsequent stage of the drilling operation to be performed, based on the acquired training data and a reward policy mapping inputs and outputs of the model. The subsequent stage of the drilling operation is performed based on the values of the drilling parameters estimated using the trained model. A difference between the estimated and actual values of the drilling parameters is calculated, based on real-time data acquired during the subsequent stage of the drilling operation. The reinforcement learning model is retrained to refine the reward policy, based on the calculated difference. At least one additional stage of the drilling operation is performed using the retrained model.

Active reinforcement learning for drilling optimization and automation

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.

Event prediction using state-space mapping during drilling operations

Aspects of the present disclosure relate to projecting control parameters of equipment associated with forming a wellbore, stimulating the wellbore, or producing fluid from the wellbore. A system includes the equipment and a computing device. The computing device is operable to project a control parameter value of the equipment using an equipment control process, and to receive confirmation that the projected control parameter value is within an allowable operating range. The computing device is also operable to adjust the equipment control process based on the confirmation, and to control the equipment to operate at the projected control parameter value. Further, the computing device is operable to receive real-time data associated with the forming of the wellbore, the stimulating of the wellbore, or the producing fluid from the wellbore. Furthermore, the computing device is operable to adjust the equipment control process based on the real-time data.

Operating wellbore equipment using a distributed decision framework

A system and method to determine closure pressure in a wellbore that can include, flowing a fracturing fluid into the wellbore during a fracturing operation of at least one stage and forming a fracture, sensing fluid pressure and a flow rate of the fracturing fluid during the fracturing operation and communicating the sensed data to a controller, plotting data points of the sensed data to a visualization device which is configured to visually present the data points to an operator as a plot, fitting a curve to the data points which represent statistically-relevant minimum pressure data at various flow rates, determining an intercept of the first curve with a zero flow rate axis of the plot, determining the closure pressure based on a pressure value of the intercept, and determining an average fracture permeability based on the closure pressure.

Determining wellbore parameters through analysis of the multistage treatments

A system and method for controlling a drilling tool inside a wellbore makes use of projection of optimal rate of penetration (ROP) and optimal controllable parameters such as weight-on-bit (WOB), and rotations-per-minute (RPM) for drilling operations. Optimum controllable parameters for drilling optimization can be predicted using a data generation model to produce synthesized data based on model physics, an ROP model, and stochastic optimization. The synthetic data can be combined with real-time data to extrapolate the data across the WOB and RPM space. The values for WOB an RPM can be controlled to steer a drilling tool. Examples of models used include a non-linear model, a linear model, a recurrent generative adversarial network (RGAN) model, and a deep neural network model.

Rate of penetration optimization for wellbores using machine learning

A drilling device may use a concurrent path planning process to create a path from a starting location to a destination location within a subterranean environment. The drilling device can receive sensor data. A probability distribution can be generated from the sensor data indicating one or more likely materials compositions that make up each portion of the subterranean environment. The probability distribution can be sampled, and for each sample, a drill path trajectory and drill parameters for the trajectory can be generated. A trained neural network may evaluate each trajectory and drill parameters to identify the most ideal trajectory based on the sensor data. The drilling device may then initiate drilling operations for a predetermined distance along the ideal trajectory.

Automated concurrent path planning and drilling parameter optimization using robotics

Growing community of inventors

Houston, TX, United States of America

Srinath Madasu