Machine Learning Model for Runtime Parameter Selection in Simulations
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Solution Overview
Problem
Current simulation methods in the oil and gas industry face inefficiencies due to the need for numerous computational iterations and resource-intensive processes, particularly when dealing with complex fluid flow equations and sudden changes in simulation inputs, leading to excessive computing resources consumption.
Innovation Solution
A method involving the generation of a machine learning model based on historical parameter values and core datasets to predict optimal runtime parameters, such as time steps and solver tolerances, for achieving simulation convergence, thereby reducing the number of computational iterations and improving resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional simulation methods are used to solve complex fluid flow equations, then simulation convergence is achieved, but excessive computing resources are consumed and numerous computational iterations are required
Solution Approach 1:
The system performs preliminary actions by training a machine learning model on historical simulation data before actual simulations. The model learns optimal runtime parameter selections from past simulations, enabling it to predict suitable parameters (such as time step sizes, convergence criteria, solver settings) for new simulations, thereby reducing iterative trials and computing resource consumption while maintaining convergence reliability
Solution Approach 2:
The system implements feedback by continuously monitoring simulation performance and using results to refine parameter selections. Historical simulation data including successful and unsuccessful parameter configurations are fed back into the machine learning model, which adjusts its predictions to optimize future simulation runs, reducing computational iterations while ensuring convergence
2Measurement precision
If traditional simulation methods are used with multiple computational iterations, then accurate modeling results are obtained, but excessive time is consumed
Solution Approach 1:
The system performs preliminary training of a machine learning model using historical simulation data that includes various runtime parameter configurations and their corresponding outcomes. This preliminary action enables the model to predict optimal parameter settings before actual simulations begin, reducing the need for multiple time-consuming iterative runs while maintaining modeling accuracy
Solution Approach 2:
The system applies parameter changes by using the machine learning model to dynamically select optimal runtime parameters (such as time step sizes, convergence tolerances, solver settings) based on learned patterns from historical data. This intelligent parameter selection reduces the number of computational iterations required while preserving simulation accuracy, thereby reducing simulation time
3Reliability
If numerous computational iterations are performed to handle sudden changes in simulation inputs, then simulation convergence is maintained, but resource utilization is inefficient
Solution Approach 1:
The system implements feedback mechanisms by continuously learning from historical simulation data, including cases with sudden input changes. The machine learning model analyzes patterns in successful parameter adjustments during such events and uses this feedback to predict optimal parameters for new simulations, reducing iterative trials and improving resource utilization efficiency while maintaining convergence reliability
Solution Approach 2:
The system replaces the traditional mechanical trial-and-error approach of adjusting runtime parameters through multiple computational iterations with an intelligent machine learning-based prediction system. This substitution enables more efficient resource utilization by predicting suitable parameters upfront, while still handling sudden changes in simulation inputs effectively
Data Source
AI summary
A method for performing a field operation of a field. The method includes obtaining historical parameter values of a runtime parameter and historical core datasets, where the historical parameter values and the historical core datasets are used for a first simulation of the field, and where each historical parameter value results in a simulation convergence during the first simulation, generating a machine learning model based at least on the historical core datasets and the historical parameter values, obtaining, during a second simulation of the field, a current core dataset, generating, using the machine learning model and based on the current core dataset, a predicted parameter value of the runtime parameter for achieving the simulation convergence during the second simulation, and completing, using at least the predicted parameter value, the second simulation to generate a modeling result of the field.