Discrete Fracture Network Realization Selection for Reservoir Simulation
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Solution Overview
Problem
Current methods for hydrocarbon exploration, particularly in identifying and extracting hydrocarbons from geological formations, face challenges in accurately modeling subsurface reservoirs due to uncertainties in fracture networks and fluid dynamics, leading to inefficiencies in production forecasting and reservoir modeling.
Innovation Solution
A computer-implemented method that generates multiple realizations of discrete fracture networks based on geological and geo-mechanical parameters, selects realizations under 10% quantile values, performs forward simulations, and produces a 3D model of the reservoir, with an objective function to reduce misfit in pressure and water production data, allowing for more accurate history matching and production forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple realizations of discrete fracture networks are generated and evaluated to improve model accuracy, then the reliability of reservoir modeling is improved, but the computational time and complexity increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-generating multiple discrete fracture network realizations with varying geometric parameters before the actual reservoir modeling process. This allows the system to have ready-to-use fracture network models that can be quickly selected and applied, reducing the computational time required during the main modeling workflow while maintaining high reliability through the use of pre-evaluated realizations
Solution Approach 2:
The patent segments the fracture network modeling process into distinct components: generating multiple realizations with different geometric parameters (length, aperture, orientation), evaluating each realization independently, and selecting the optimal realization for reservoir modeling. This segmentation allows parallel processing of different realizations and reduces overall computational complexity by breaking down the monolithic modeling task into manageable stages
2Manufacturing precision
If comprehensive geological and geo-mechanical parameters are incorporated to improve subsurface representation, then the manufacturing precision of the reservoir model is improved, but the device complexity increases
Solution Approach 1:
The patent applies parameter changes by systematically varying key geometric parameters of the discrete fracture networks (such as fracture length, aperture, orientation, and density) to generate multiple realizations. This allows the system to explore different subsurface scenarios and select the realization that best matches observed reservoir behavior, improving subsurface representation accuracy while managing complexity through controlled parameter variation rather than uncontrolled model complexity
Solution Approach 2:
The patent applies local quality by incorporating spatially varying geological and geo-mechanical parameters specific to different regions of the reservoir. Different fracture network realizations are generated with locally appropriate parameters based on regional geological conditions, allowing high precision in representing local subsurface heterogeneity while avoiding the need for a uniformly complex model across the entire reservoir
3Measurement precision
If iterative forward simulations are performed to reduce misfit in pressure and production data, then the measurement precision of reservoir parameters is improved, but the productivity of the modeling process decreases
Solution Approach 1:
The patent applies partial action by performing forward simulations on a selected subset of discrete fracture network realizations rather than all possible realizations. The system generates multiple realizations, evaluates them using geological and geo-mechanical criteria, and then performs iterative forward simulations only on the most promising candidates. This reduces the total number of computationally expensive forward simulations required while still achieving adequate calibration accuracy for pressure and production data
Solution Approach 2:
The patent applies feedback by using the results of forward simulations (misfit in pressure and production data) to iteratively refine the selection of fracture network realizations. The system evaluates how well each realization matches observed reservoir behavior and uses this feedback to guide subsequent simulation efforts, focusing computational resources on the most promising realizations and improving modeling efficiency through iterative learning
Data Source
AI summary
The present disclosure describes a computer-implemented method that includes: receiving a seismic dataset of a surveyed subsurface of a reservoir, the seismic dataset comprising observed pressure and production data of the reservoir as well as a set of geological and geo-mechanical parameters representing physical features of the surveyed subsurface; generating multiple realizations of a discrete fracture network (DFN) based on a subset of the set of geological and geo-mechanical parameters; selecting, from the multiple realizations, one or more realizations based on a parameter with a value under a 10% quantile of a full range of likely values; performing a forward simulation for the reservoir based on the selected one or more realizations and the observed pressure and production data; determining that a misfit of the forward simulation is below a threshold based on evaluating an objective function; and producing a model of the reservoir based on the forward simulation.