Bayesian Decline Curve Estimation for Faster EUR Forecasting
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Solution Overview
Problem
Traditional decline curve analyses for hydrocarbon production are manual, subjective, and time-consuming, leading to inconsistencies and delays in updating estimated ultimate recovery (EUR) for thousands of wells, and do not adequately account for statistical significance and uncertainty.
Innovation Solution
A Bayesian statistical approach is employed to automate decline curve analysis, using a computer-implemented method that includes clustering historical production data, generating initial and posterior probability distributions, and applying Bayes' theorem to estimate EUR, thereby improving the accuracy and efficiency of hydrocarbon resource estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual decline curve analysis is used, then subjectivity and inconsistency are introduced, but automation and efficiency are reduced
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated computer-implemented Bayesian statistical system. The system automatically processes production data, performs clustering, generates probability distributions, and calculates EUR estimates without human intervention, thereby eliminating subjectivity while maintaining high productivity through automation.
Solution Approach 2:
The system performs self-service by automatically updating EUR estimates for thousands of wells without requiring manual intervention. The automated pipeline continuously processes new production data, recalculates decline curves, and updates reservoir models, enabling the system to serve itself and eliminate the need for repetitive manual analysis.
2Reliability
If traditional manual analysis is used, then time consumption increases, but statistical significance and uncertainty quantification are reduced
Solution Approach 1:
The patent replaces time-consuming manual statistical analysis with automated Bayesian computational methods. The system efficiently calculates posterior probability distributions, quantifies uncertainty through credible intervals, and assesses statistical significance using computational algorithms that process data rapidly without manual intervention.
Solution Approach 2:
The system performs preliminary actions by pre-processing production data, automatically detecting decline regimes, and generating prior probability distributions before formal Bayesian analysis. This preliminary preparation enables rapid subsequent analysis and reduces the time required for full EUR estimation while maintaining statistical rigor.
3Measurement precision
If comprehensive uncertainty analysis is performed, then accuracy of EUR estimates improves, but computational complexity increases
Solution Approach 1:
The patent segments the complex uncertainty analysis into distinct modular components: data preprocessing and smoothing, clustering algorithms for regime identification, Bayesian parameter estimation modules, and uncertainty quantification stages. Each module handles a specific aspect of the analysis independently, reducing overall system complexity while maintaining comprehensive accuracy.
Solution Approach 2:
The system manages complexity by dynamically adjusting parameter representations throughout the analysis pipeline. It transforms raw production data into standardized formats, converts uncertainty measurements into probability distributions, and adapts model parameters based on detected decline regimes, thereby simplifying the analysis at each stage while preserving accuracy.
Data Source
AI summary
Systems and methods are provided for performing decline curve analysis. The system can obtain historical production data as a function of time for at least one well drilled into a reservoir. The data can be smoothed and clustered into at least one cluster corresponding to a region of the reservoir. For the region, the system can generate an initial probability distribution for each decline parameter in a corresponding decline curve model and apply a Bayesian function iteratively to each initial probability distribution to generate a posterior probability distribution for each decline parameter to estimate an expected ultimate recovery (EUR) for each well. The system can generate a graphical representation of each posterior distribution for each well and display the graphical representations on a display.