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公开(公告)号：US20200277851A1
公开(公告)日：2020-09-03
申请号：US16754233
申请日：2017-11-13
Applicant: LANDMARK GRAPHICS CORPORATION
Inventor： Srinath MADASU , Keshava Prasad RANGARAJAN
Abstract: Aspects of the present disclosure relate to receiving data associated with a subterranean reservoir to be penetrated by a wellbore and training a neural network with both the data and a physics-based first principles model. The neural network is then used to make predictions regarding the properties of the subterranean reservoir, and these predictions are in turn used to determine one or more controllable parameters for equipment associated with a wellbore. The controllable parameters can then be used to control equipment for formation, stimulation, or production relative to the wellbore.
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2.发明申请公开(公告)号：US20220112799A1
公开(公告)日：2022-04-14
申请号：US17279969
申请日：2020-04-13
Applicant: Landmark Graphics Corporation
Inventor： Da PANG , Srinath MADASU , Xinli JIA , Keshava Prasad RANGARAJAN
Abstract: 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.
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公开(公告)号：US20210133375A1
公开(公告)日：2021-05-06
申请号：US17014331
申请日：2020-09-08
Applicant: Landmark Graphics Corporation
Inventor： Yevgeniy ZAGAYEVSKIY , Shohreh AMINI , Srinath MADASU , Zhi CHAI , Azor NWACHUKWU
IPC: G06F30/27
Abstract: 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.
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4.发明公开公开(公告)号：US20240093605A1
公开(公告)日：2024-03-21
申请号：US17766775
申请日：2019-11-07
Applicant: LANDMARK GRAPHICS CORPORATION
Inventor： Travis St. George RAMSAY , Egidio MAROTTA , Srinath MADASU
CPC classification number: E21B49/0875 , E21B43/162 , E21B2200/22
Abstract: The present disclosure is related to improvements in methods for evaluating and predicting responses of virtual sensors to determine formation and fluid properties as well as classifying the predicted as plausible or outlier responses that can indicate the need for maintenance of downhole physical sensors. In one aspect, a method includes detecting a change to a system of operating a wellbore to yield a determination, the system including a virtual sensor, the virtual sensor including a physical sensor placed in the wellbore for collecting one or more physical properties inside the wellbore; and based on the determination, performing one of retraining a machine learning model for predicting an output of the virtual sensor or predicting an output of the virtual sensor using the machine learning mode, the predicted output being indicative of at least one of sub-surface formation or fluid properties inside the wellbore.
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公开(公告)号：US20220298917A1
公开(公告)日：2022-09-22
申请号：US17612363
申请日：2019-07-18
Applicant: LANDMARK GRAPHICS CORPORATION
Inventor： Travis St. George RAMSAY , Egidio MAROTTA , Srinath MADASU
Abstract: The present disclosure is related to improvements in methods for evaluating formation fluid properties of interest in an in-production wellbore as well as evaluating health and functionalities of physical sensors present in and collecting data within the well. In one aspect, a method includes receiving data from one or more physical sensors within a wellbore; determining at least one formation property of the wellbore using one or more machine learning models receiving the data as input and generating reservoir simulation models using the at least one formation property.
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Patent Agency Ranking
公开(公告)号：US20210404302A1
公开(公告)日：2021-12-30
申请号：US16474185
申请日：2018-08-09
Applicant: Landmark Graphics Corporation
Inventor： Srinath MADASU , Terry WONG , Keshava Prasad RANGARAJAN , Steven WARD , ZhiXiang JIANG
Abstract: A system and method for controlling a gas supply to provide gas lift for a production wellbore makes use of Bayesian optimization. A computing device controls a gas supply to inject gas into one or more wellbores. The computing device receives reservoir data associated with a subterranean reservoir to be penetrated by the wellbores and can simulate production using the reservoir data and using a physics-based or machine learning or hybrid physics-based machine learning model for the subterranean reservoir. The production simulation can provide production data. A Bayesian optimization of an objective function of the production data subject to any gas injection constraints can be performed to produce gas lift parameters. The gas lift parameters can be applied to the gas supply to control the injection of gas into the wellbore or wellbores.
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公开(公告)号：US20210164944A1
公开(公告)日：2021-06-03
申请号：US16956605
申请日：2019-07-23
Applicant: Landmark Graphics Corporation
Inventor： Srinivasan JAGNNATHAN , Oluwatosin OGUNDARE , Srinath MADASU , Keshava RANGARAJAN
Abstract: Methods and systems for solving inverse problems arising in systems described by a physics-based forward propagation model use a Bayesian approach to model the uncertainty in the realization of model parameters. A Generative Adversarial Network (“GAN”) architecture along with heuristics and statistical learning is used. This results in a more reliable point estimate of the desired model parameters. In some embodiments, the disclosed methodology may be applied to automatic inversion of physics-based modeling of pipelines.
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公开(公告)号：US20220284310A1
公开(公告)日：2022-09-08
申请号：US17624974
申请日：2019-08-09
Applicant: LANDMARK GRAPHICS CORPORATION
Inventor： Srinath MADASU , Keshava Prasad RANGARAJAN
Abstract: A model optimizer for predicting a drill bit variable can select a model from multiple models based on a learned preference. The preference may be updated according to preference indicator received from a user in response to an output model selection.
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公开(公告)号：US20220275714A1
公开(公告)日：2022-09-01
申请号：US17628610
申请日：2019-08-30
Applicant: LANDMARK GRAPHICS CORPORATION
Inventor： Srinath MADASU , Keshava Prasad RANGARAJAN
IPC: E21B43/16
Abstract: Aspects of the subject technology relate to systems and methods for predicting physical characteristics of a physical environment using a physical characterization model trained based on simulated states of a modeled physical environment. A physical characterization model can be generated based on a plurality of simulated states of a modeled physical environment. Specifically, the physical characterization model can be trained by mapping simulated spatial properties of the modeled physical environment temporally across the plurality of simulated states of the modeled physical environment. Further, input state data describing one or more input states of a physical environment can be received. One or more physical characteristics of the physical environment can be predicted by applying the physical characterization model to the one or more input states of the physical environment.
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10.发明申请公开(公告)号：US20200284944A1
公开(公告)日：2020-09-10
申请号：US16640300
申请日：2019-03-04
Applicant: LANDMARK GRAPHICS CORPORATION
Inventor： Srinath MADASU , Hanife Meftun ERDOGAN , Keshava Prasad RANGARAJAN
Abstract: A system for determining completion parameters for a wellbore includes a sensor and a computing device. The sensor can be positioned at a surface of a wellbore to detect data prior to finishing a completion stage for the wellbore. The computing device can receive the data, perform a history match for simulation and production using the sensor data and historical data, generate inferred data for completion parameters using the historical data identified during the history match, predict stimulated area and production by inputting the inferred data into a neural network model, determine completion parameters for the wellbore using Bayesian optimization on the stimulated area and production from the neural network model, profit maximization, and output the completion parameters for determining completion decisions for the wellbore.
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