Landmark Graphics Corporation

Avinash Wesley

Keshava Prasad Rangarajan

Joseph Blake Winston

Peter C Yu

Brent Charles Houchens

Feifei Zhang

Robello Samuel

Srinath Madasu

Andrey V Filippov

Olivier Germain

Jianxin Lu

Manish Kumar Mittal

Feifei Zhang, Jr

Giulia Toti

Aimee Jackson Taylor

Greg Daniel Brumbaugh

Janaki Vamaraju

Jonathan Nguyen

Andrew Shane Elsey

Youpeng Huang

Ethan Myers

Kaiji Lu

Michael Keith Redman

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

Aspects and features of this disclosure relate to projecting physical drilling parameters to control a drilling operation. A computing system applies Bayesian optimization to a model incorporating the input data using varying values for an adverse drilling factor to produce a target function. The computing system determines a minimum value for the target function. The computing system provides a projected value for the physical drilling parameters based on the minimum value. The computing system generates an alert responsive to determining that the projected value for the physical drilling parameters exceeds a prescribed limit.

Physical parameter projection for wellbore drilling

Systems, methods, and computer-readable media are described for intelligent, real-time monitoring and managing of changes in oilfield equilibrium to optimize production of desired hydrocarbons and economic viability of the field. In some examples, a method can involve generating, based on a topology of a field of wells, a respective graph for the wells, each respective graph including computing devices coupled with one or more sensors and/or actuators. The method can involve collecting, via the computing devices, respective parameters associated with one or more computing devices, sensors, actuators, and/or models, and identifying a measured state associated with the computing devices, sensors, actuators, and/or models. Further, the method can involve automatically generating, based on the respective graph and respective parameters, a decision tree for the measured state, and determining, based on the decision tree, an automated adjustment for modifying production of hydrocarbons and/or an economic parameter of the hydrocarbon production.

Intelligent, real-time response to changes in oilfield equilibrium

A drill bit subsystem can include a drill bit, a processor, and a non-transitory computer-readable medium for storing instructions and for being positioned downhole with the drill bit. The instructions of the non-transitory computer-readable medium can include a machine-teachable module and a control module that are executable by the processor. The machine-teachable module can receive depth data and rate of drill bit penetration from one or more sensors in a drilling operation, and determine an estimated lithology of a formation at which the drill bit subsystem is located. The control module can use the estimated lithology to determine an updated location of the drill bit subsystem, and control a direction of the drill bit using the updated location and a drill plan.

Drill bit subsystem for automatically updating drill trajectory

Tight spots in movements of a drill string in an oil well are identified by comparing a large interval hookload moving average to a short interval hookload moving average, comparing a large interval bit depth moving average to a short interval bit depth moving average, and DBSCANing the tight spots to identify a fully-stuck event.

Stuck pipe detection

An example method includes receiving a data set containing combinations of drilling parameter values and operating condition values for a drilling system corresponding to each combination of drilling parameter values. At least one of a frequency and a duration of use may be determined for each of the combinations of drilling parameter values in the data set. For at least some of the combinations of drilling parameter values, a contour map identifying the combinations of drilling parameter values, the operating condition values corresponding to the combinations of drilling parameter values, and at least one of the frequency and the duration of use may be displayed for at least some of the combinations of drilling parameter values.

Real-time performance analyzer for drilling operations

Growing community of inventors

Houston, TX, United States of America

Avinash Wesley