Landmark Graphics Corporation

Yogendra Narayan Pandey

Jeffrey Marc Yarus

Rae Mohan Srivastava

Keshava Prasad Rangarajan

Genbao Shi

Veronica Liceras

Zhaoyang Wang

Srinath Madasu

Joseph Blake Winston

Jin Fei

Xi Wang

Nagaraj Srinivasan

Aimee Jackson Taylor

Naresh Chaudhary

Yevgeniy Zagayevskiy

Jeffery Padgett

Wei Chiu

James Etienne

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

Systems and methods for modeling petroleum reservoir properties using a gridless reservoir simulation model are provided. Data relating to geological properties of a reservoir formation is analyzed. A tiered hierarchy of geological elements within the reservoir formation is generated at different geological scales, based on the analysis. The geological elements at each of the different geological scales in the tiered hierarchy are categorized. Spatial boundaries between the categorized geological elements are defined for each of the geological scales in the tiered hierarchy. A scalable and updateable gridless model of the reservoir formation is generated, based on the spatial boundaries defined for at least one of the geological scales in the tiered hierarchy.

Point-vector based modeling of petroleum reservoir properties for a gridless reservoir simulation model

Embodiments of the subject technology for deep learning based reservoir modelling provides for receiving input data comprising information associated with one or more well logs in a region of interest. The subject technology determines, based at least in part on the input data, an input feature associated with a first deep neural network (DNN) for predicting a value of a property at a location within the region of interest. Further, the subject technology trains, using the input data and based at least in part on the input feature, the first DNN. The subject technology predicts, using the first DNN, the value of the property at the location in the region of interest. The subject technology utilizes a second DNN that classifies facies based on the predicted property in the region of interest.

Deep learning based reservoir modeling

A method for fracturing a formation is provided. Real-time fracturing data is acquired from a well bore during fracturing operation. The real-time fracturing data is processed using a recurrent neural network trained using historical data from analogous wells. A real-time response variable prediction is determined using the processed real-time fracturing data. Fracturing parameters for the fracturing operation are adjusted in real-time based on the real-time response variable prediction. The fracturing operation is performed using the fracturing parameters that were adjusted based on the real-time response variable prediction.

Recurrent neural network model for bottomhole pressure and temperature in stepdown analysis

Target objects are simulated using different triangle mesh sizes to improve processing performance. To perform the simulation, a seed point for the target object within a constraint volume is determined, the seed point representing a vertex of a first triangle forming part of the target object. One or more hexagonal orbits of triangles adjacent the first triangle are propagated, whereby the hexagonal orbits of triangles form the target object. The size of each triangle is determined based upon dimensions of the target object, and the target object is generated.

Target object simulation using orbit propagation

Fracture networks are simulated using a large triangle mesh size for large fractures and a smaller triangle mesh size for small fractures. Input data defining parameters of one or more fractures are input, the fractures being comprised of a triangle mesh. A first triangle mesh size for the fractures is determined based upon the input data. A second smaller triangle mesh size is then determined based upon the input data. The fracture network is then simulated using the large and small triangle mesh sizes.

Fracture network triangle mesh adjustment

Target objects having undulating surfaces are simulated using different triangle mesh sizes to improve processing performance. To perform the simulation, a target object is generated using a triangle mesh formed by a plurality of triangles. The target object has an X, Y, and Z direction, wherein the Z direction is perpendicular to an X-Y plane of the target object. The undulating surface on the target object is generated using a Z value in the Z direction.

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Houston, TX, United States of America

Yogendra Narayan Pandey