Downhole Pressure Testing with Sequential Bayesian Location Selection
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Solution Overview
Problem
Pressure testing operations in oil and gas exploration are time-consuming and often result in inadequate or insufficient data quality, with no effective recourse for remedial action if the quality of formation pressure tests is found to be inadequate.
Innovation Solution
A Bayesian Framework is utilized to identify optimal formation pressure testing locations by maximizing information gain, using previously acquired log data and a recursive workflow to update prior distributions into posterior distributions, determining the best sampling locations based on expected information gain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If over-sampling a formation is performed to ensure adequate data quality, then data quality is improved, but time consumption increases
Solution Approach 1:
The patent applies preliminary action by using a Bayesian framework to predict optimal sampling locations before actual pressure testing occurs. The system calculates probability distributions for formation pressure at multiple candidate locations and selects the most informative locations in advance, avoiding the need for exhaustive sampling while ensuring data quality. This resolves the contradiction by determining adequate sampling locations proactively rather than through time-consuming trial-and-error sampling.
Solution Approach 2:
The patent changes the parameter of sampling strategy from fixed/random to adaptive/optim化的 based on Bayesian probability calculations. By dynamically adjusting which locations to sample based on expected information gain, the system achieves adequate data quality with fewer samples, thereby reducing time consumption while maintaining measurement precision.
2Loss of time
If under-sampling a formation is performed to reduce time consumption, then time consumption is reduced, but data quality becomes insufficient
Solution Approach 1:
The patent implements feedback through its Bayesian framework that continuously updates probability distributions based on acquired measurements. After each pressure test, the system recalculates the posterior distribution and uses it to determine the next most informative sampling location. This feedback mechanism ensures that each subsequent measurement maximizes information gain, allowing the system to achieve adequate data quality with minimal sampling by adaptively focusing on the most informative locations.
Solution Approach 2:
The sampling strategy transitions from static to dynamic through the Bayesian framework. The system continuously adapts the selection of sampling locations based on accumulated information, making the sampling process dynamic and responsive to actual formation characteristics. This dynamic approach ensures adequate data quality with fewer samples by concentrating measurements where they provide maximum information gain.
3Loss of information
If sequential selection based on Bayesian Framework is implemented to maximize information gain, then information gain is improved, but computational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the formation into discrete candidate sampling locations and evaluating each location's expected information gain independently through Bayesian calculations. The system segments the complex problem of optimal sampling into manageable evaluations of individual locations, selecting the top candidates based on their expected contribution to information gain. This segmentation approach maximizes information gain while keeping computational complexity manageable by focusing calculations on discrete, evaluable units.
Data Source
AI summary
A method may comprise disposing a downhole pressure sampling tool into a wellbore; calculating a prior distribution based at least in part on one or more proxy logs, moving the downhole pressure sampling tool to a first location in the wellbore, taking at least one measurement with the downhole pressure sampling tool at the first location in the wellbore, calculating a posterior distribution based at least in part on the at least one measurement and the prior distribution, identifying a second location based at least in part on the posterior distribution, and moving the downhole pressure sampling tool to the second location.