EVENT PREDICTION USING STATE-SPACE MAPPING DURING DRILLING OPERATIONS

    公开(公告)号:US20220003108A1

    公开(公告)日:2022-01-06

    申请号:US17256164

    申请日:2020-02-03

    Abstract: 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.

    SHALE FIELD WELLBORE CONFIGURATION SYSTEM

    公开(公告)号:US20210388700A1

    公开(公告)日:2021-12-16

    申请号:US16899816

    申请日:2020-06-12

    Abstract: Aspects and features of a system for providing parameters for shale field configuration include a processor, and instructions that are executable by the processor. The system, using the processor, can receive resource supply data associated with a shale field to be penetrated by a wellbore or wellbores and simulate production from the shale field using the resource supply data to determine constraints and decision variables. The system can optimize a multi-objective function of the decision variables subject to the constraints to produce controllable parameters for operating the shale field. As examples, these parameters may be related to formation or stimulation of the wellbore or wellbores at the shale field site.

    Multi-well drilling optimization using robotics

    公开(公告)号:US12091959B2

    公开(公告)日:2024-09-17

    申请号:US17616227

    申请日:2019-07-10

    CPC classification number: E21B44/02 E21B2200/22

    Abstract: 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.

    Active reinforcement learning for drilling optimization and automation

    公开(公告)号:US11982171B2

    公开(公告)日:2024-05-14

    申请号:US17047109

    申请日:2020-06-05

    CPC classification number: E21B44/00 G06N3/092 E21B2200/20 E21B2200/22

    Abstract: 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.

    MULTI-WELL DRILLING OPTIMIZATION USING ROBOTICS

    公开(公告)号:US20220235645A1

    公开(公告)日:2022-07-28

    申请号:US17616227

    申请日:2019-07-10

    Abstract: 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.

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