Intelligent Well Placement Optimization for Unconventional Reservoirs
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Solution Overview
Problem
Oil and gas field operators face challenges in maintaining production levels due to variable PVT behavior and declining production in unconventional gas basins with ultra-low permeability reservoirs, requiring the optimization of well placement and scheduling to meet production objectives.
Innovation Solution
A method that involves receiving grid data for a hydrocarbon reservoir, discretizing the region into blocks, determining deliverability magnitude based on permeability and net pay, proposing well locations, forecasting production, and selecting/scheduling wells to meet production objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If additional wells are drilled to maintain production targets, then production level is maintained, but capital expenditure and facility constraints are increased
Solution Approach 1:
The system changes parameters such as well location coordinates, drilling depth, and completion design to optimize production while controlling costs. By adjusting these parameters systematically, the method identifies configurations that maintain production targets with minimized capital expenditure.
Solution Approach 2:
The system performs preliminary analysis and planning before actual drilling by using machine learning models to predict production outcomes and identify optimal well locations. This preliminary action prevents unnecessary drilling and reduces capital expenditure by selecting only the most promising well sites.
2Productivity
If well placement is optimized to meet production objectives, then production efficiency is improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The system uses machine learning models that are trained on historical data and then copied/deployed to make rapid predictions for new well locations. This approach avoids complex real-time simulations while maintaining high predictive accuracy, thus improving production efficiency without excessive computational complexity.
Solution Approach 2:
The computational problem is segmented into distinct stages: data preprocessing, feature extraction, model training, and prediction. This segmentation allows each stage to be optimized independently and enables parallel processing, reducing overall computational complexity while maintaining production efficiency.
Data Source
AI summary
A method to determine locations of new wells that includes receiving grid data for a region containing a hydrocarbon reservoir and discretizing the region into a plurality of blocks. The method further includes receiving optimization parameters that include at least one production objective, where the production objective specifies a desired hydrocarbon production from the hydrocarbon reservoir over a period of time and determining a deliverability magnitude for each block in the plurality of blocks based on the grid data, where the deliverability magnitude is based on a permeability and a net pay for each block. The method further includes proposing one or more proposed well locations based on the deliverability magnitude, forecasting the production through time of the one or more proposed well locations, and selecting and scheduling one or more proposed well locations to meet the at least one production objective based on the forecasted production.