公开(公告)号：US20200173269A1

公开(公告)日：2020-06-04

申请号：US16631542

申请日：2017-08-21

Applicant: LANDMARK GRAPHICS CORPORATION

Inventor： Srinath MADASU ,  Keshava Prasad RANGARAJAN ,  Robello SAMUEL ,  Nishant RAIZADA

IPC: E21B44/00 ,  E21B7/04

Abstract: A system for real-time steering of a drill bit includes a drilling arrangement and a computing device in communication with the drilling arrangement. The system iteratively, or repeatedly, receives new data associated the wellbore. At each iteration, a model, for example an engineering model, is applied to the new data to produce an objective function defining the selected drilling parameter. The objective function is modified at each iteration to provide an updated value for the selected drilling parameter and an updated value for at least one controllable parameter. In one example, the function is modified using Bayesian optimization The system iteratively steers the drill bit to obtain the updated value for the selected drilling parameter by applying the updated value for at least one controllable parameter over the period of time that the wellbore is being formed.

公开(公告)号：US20210404315A1

公开(公告)日：2021-12-30

申请号：US16652336

申请日：2019-05-16

Applicant: Landmark Graphics Corporation

Inventor： Mahdi PARAK ,  Srinath MADASU ,  Egidio MAROTTA

IPC: E21B44/02

Abstract: Systems and methods can automatically and dynamically determine an optimum frequency for data being input into a drilling optimization tool in order to provide predictive modeling for well drilling operations. The methods and systems selectively input sets of data having different frequencies into the drilling optimization tool to build different predictive models at different frequencies. An optimization algorithm such as Bayesian optimization is then applied to the models to identify in real time an optimum frequency for the data sets being input into the drilling optimization tool based on current operational and environmental parameters.

公开(公告)号：US20210131260A1

公开(公告)日：2021-05-06

申请号：US17014944

申请日：2020-09-08

Applicant: Landmark Graphics Corporation

Inventor： Yevgeniy ZAGAYEVSKIY ,  Shohreh AMINI ,  Srinath MADASU ,  Zhi CHAI ,  Azor NWACHUKWU

IPC: E21B47/003 ,  E21B21/08 ,  G01V99/00 ,  G06K9/62 ,  G06F30/20

Abstract: 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. This subset has a reduced parameter space than that of the input variables. The subset is generated using a function decomposition and a design of experiments (sensitivity analysis) to reduce number of original variables and identify original or transformed input variables that can be used to approximate output 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.

公开(公告)号：US20210047910A1

公开(公告)日：2021-02-18

申请号：US17047230

申请日：2018-05-09

Applicant: Landmark Graphics Corporation

Inventor： Srinath MADASU ,  Keshava Prasad RANGARAJAN

IPC: E21B44/06 ,  E21B45/00 ,  E21B21/08 ,  G06N3/04 ,  G06N20/10

Abstract: A method for optimizing real time drilling with learning uses a multi-layer Deep Neural Network (DNN) built from input drilling data. A plurality of drilling parameter features is extracted using the DNN. A linear regression model is built based on the extracted plurality of drilling parameter features. The linear regression model is applied to predict one or more drilling parameters.

公开(公告)号：US20210017845A1

公开(公告)日：2021-01-21

申请号：US17043129

申请日：2018-04-12

Applicant: Landmark Graphics Corporation

Inventor： Srinath MADASU ,  Yogendra Narayan PANDEY ,  Keshava RANGARAJAN

IPC: E21B43/26 ,  G06N3/04 ,  G06N3/063 ,  E21B47/06

Abstract: 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.

公开(公告)号：US20200240257A1

公开(公告)日：2020-07-30

申请号：US16754850

申请日：2018-10-15

Applicant: LANDMARK GRAPHICS CORPORATION

Inventor： Srinath MADASU ,  Keshava Prasad RANGARAJAN

IPC: E21B44/00

Abstract: A system and method for controlling a drilling tool inside a wellbore makes use of simulated annealing and Bayesian optimization to determine optimum controllable drilling parameters. In some aspects, a computing device generates sampled exploration points using simulated annealing and runs a Bayesian optimization using a loss function and the exploration points to optimize at least one controllable drilling parameter to achieve a predicted value for a selected drilling parameter. In some examples, the selected drilling parameter is rate-of-penetration (ROP) and in some examples, the controllable drilling parameters include such parameters as rotational speed (RPM) and weight-on-bit (WOB). In some examples, the computing device applies the controllable drilling parameter(s) to the drilling tool to achieve the predicted value for the selected drilling parameter and provide real-time, closed-loop control and automation in drilling.

公开(公告)号：US20200210841A1

公开(公告)日：2020-07-02

申请号：US16631429

申请日：2017-09-28

Applicant: LANDMARK GRAPHICS CORPORATION

Inventor： Srinath MADASU ,  Keshava Prasad RANGARAJAN

IPC: G06N3/08 ,  E21B43/26 ,  E21B47/00

Abstract: A system for multi-stage placement of material in a wellbore includes a recurrent neural network that can be configured based on data from a multi-stage, stimulated wellbore. A computing device in communication with a sensor and a pump is operable to implement the recurrent neural network, which may include a long short-term neural network model (LSTM). Surface data from the sensor at each observation time of a plurality of observation times is used by the recurrent neural network to produce a predicted value for a response variable at a future time, and the predicted value for the response variable is used to control a pump being used to place the material.