Formation Pressure Testing Optimization Using Memoized Scenarios
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Accurately characterizing subsurface formations such as reservoirs is challenging due to the need to optimally determine and combine subsurface parameters like porosity and fluid permeability, considering structural relationships between primary and secondary structures, and accounting for geological features and energy systems.
Innovation Solution
A method for pressure testing that involves determining distribution data, generating test scenarios, combining fluid rate and volume data to create pressure curves, and using convergence data to optimize energy exploration equipment configuration, with memoization to enhance efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pressure testing methods are used to characterize subsurface formations, then measurement data can be obtained, but computational costs and time required for analyzing multiple subsurface parameters are excessively high
Solution Approach 1:
The system performs preliminary generation of test scenarios and pressure curves based on distribution data before actual pressure testing. By pre-computing multiple test scenarios with different fluid rates and volumes, the system prepares optimization data in advance, significantly reducing the computational time required during actual field operations while maintaining accurate subsurface characterization.
Solution Approach 2:
The system creates simplified models and representations of complex subsurface scenarios through generated test scenarios. Instead of directly analyzing complex real-world subsurface data, the system uses generated pressure curves from test scenarios that replicate subsurface conditions, enabling efficient computation while preserving measurement precision through the use of convergence data to validate results.
2Measurement precision
If comprehensive subsurface parameter analysis is performed to account for geological features and structural relationships, then characterization accuracy improves, but computational complexity increases
Solution Approach 1:
The system segments the complex subsurface characterization problem into distinct test scenarios, each focusing on specific fluid rates and volumes. By dividing the comprehensive parameter analysis into multiple discrete pressure test scenarios with specific combinations of fluid rate data and fluid volume data, the system manages computational complexity while maintaining overall characterization accuracy through aggregation of results.
Solution Approach 2:
The system systematically varies key parameters such as fluid rate and fluid volume across different test scenarios to optimize pressure testing. By changing these parameters in controlled ways and analyzing the resulting pressure curves, the system determines optimal parameters for energy exploration equipment without requiring equally complex variations in all subsurface parameters, thus reducing computational complexity while preserving accuracy.
3Reliability
If multiple pressure tests are conducted to ensure accurate formation pressure measurement, then measurement reliability improves, but the number of required tests and associated costs increase
Solution Approach 1:
The system uses convergence data as feedback to determine when sufficient pressure testing has been performed. By analyzing whether pressure curves from different test scenarios converge to consistent formation pressure values, the system objectively determines when measurement reliability has been achieved, preventing unnecessary additional tests while ensuring adequate validation through the convergence criterion.
Solution Approach 2:
The system generates and evaluates multiple test scenarios beyond what a single traditional pressure test would provide, but uses convergence analysis to identify the sufficient subset. By performing more tests than minimally required and then using convergence data to determine when reliability is achieved, the system ensures robust measurement reliability while improving productivity by stopping tests once convergence is demonstrated rather than requiring a fixed large number of tests.
Data Source
AI summary
The disclosed methods include: determining distribution data for a subsurface environment of interest; generating, based on the distribution data, a set of test scenarios; combining, based on the distribution data and a first test scenario comprised in the set of test scenarios, a first combination of fluid rate data and fluid volume data; combining, based on the distribution data and a second test scenario comprised in the set of test scenarios, a second combination of fluid rate data and fluid volume data; generating, based on the first combination of fluid rate data and fluid volume data, a first pressure curve; generating, based on the second combination of fluid rate data and fluid volume data, a second pressure curve; determining, based on the first pressure curve or the second pressure curve, convergence data; generating, based on the convergence data, optimal data values for configuring energy exploration equipment.