Probabilistic Area Identification for Well Placement Under Uncertainty
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Solution Overview
Problem
Well placement planning in the oil and gas industry is a time-consuming and inefficient process due to its manual nature and the difficulty in objectively exploring the complete solution space under uncertainty, particularly in predicting subsurface reservoir properties with localized and uncertain data.
Innovation Solution
A probabilistic approach is employed to identify areas of interest by generating a combined probability map from multiple reservoir model realizations, using opportunity maps to calculate and store probability values based on recovery potential, and then selecting areas for well placement, which includes screening redundant models and calibrating them through history matching and image analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a manual process is used for well placement planning, then users can perform reservoir simulation forecasts and calculate predicted performance, but the process becomes time-consuming and computationally inefficient
Solution Approach 1:
The system performs self-service by automatically executing reservoir simulation forecasts and calculating predicted performance without requiring manual user intervention for each iteration. The automated workflow includes generating multiple reservoir model realizations, calculating opportunity maps, and identifying areas of interest automatically based on predefined criteria.
Solution Approach 2:
The manual mechanical process of repeated user iterations is replaced with an automated computational system that uses probabilistic methods and computer algorithms to perform reservoir simulation forecasts and optimize well placement plans, substituting human manual operations with automated computational mechanisms.
2Adaptability or versatility
If manual iteration is used to explore solution space, then users can modify well locations and completions, but it becomes difficult to objectively explore the complete solution space
Solution Approach 1:
The system dynamically generates multiple reservoir model realizations and automatically iterates through different well placement scenarios based on probabilistic criteria. The opportunity maps are dynamically updated as new realizations are generated, allowing the system to adaptively explore the solution space objectively without manual intervention.
Solution Approach 2:
The system incorporates feedback mechanisms where the results of reservoir simulation forecasts are automatically fed back into the optimization process. The probability values from opportunity maps provide feedback that guides the automatic selection of areas of interest, ensuring objective and reliable exploration of the complete solution space.
3Measurement precision
If localized data from well logging is used to predict subsurface features, then data collection is straightforward, but uncertainty increases significantly with distance from the data collection point
Solution Approach 1:
The reservoir is segmented into multiple discrete cells, each with its own properties. By generating multiple reservoir model realizations that account for uncertainty in each cell, the system can propagate and manage the uncertainty from localized data points throughout the entire reservoir model, maintaining measurement precision while acknowledging the loss of information with distance.
Data Source
AI summary
A method, apparatus, and program product utilize a probabilistic approach to identify areas of interest from multiple realizations of a reservoir model to drive well placement planning under uncertainty. A combined probability map may be generated from opportunity maps generated for multiple reservoir model realizations such that a probability value in various entries of the probability map represents a probability of opportunity values stored in corresponding entries of the opportunity maps meeting an opportunity criterion. One or more areas of interest may then be identified from the probability map.