Reservoir Modeling Using Elastic Property Constraints
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Solution Overview
Problem
The challenge in subsurface reservoir modeling is determining elastic properties of well reservoirs, which is challenging due to limited data availability, particularly when detailed lithological information is difficult to obtain, affecting hydrocarbon removal efficiency from subterranean formations.
Innovation Solution
A reservoir modeling system that uses a constrained meta-heuristic optimization module to characterize the distribution of pseudo-components by analyzing well log data and seismic survey data, applying elastic property constraints to determine lithologies and elastic properties, even in data-scarce conditions, through a combination of clustering algorithms and meta-heuristic optimization techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed lithological information is obtained through traditional methods (outcrop analysis, core data, well logging), then elastic properties can be determined more accurately, but the cost, time, and complexity of data acquisition increase significantly
Solution Approach 1:
The patent uses seismic attributes as intermediary parameters that can be derived from readily available seismic data. These attributes serve as proxies for direct lithological measurements, enabling elastic property determination without requiring complex core analysis or well logging operations. The seismic attributes mediate between the easily obtained seismic data and the difficult-to-obtain elastic properties.
Solution Approach 2:
The patent replaces mechanical/physical measurement systems (core sampling, well logging tools) with a computational system based on seismic attribute analysis and machine learning algorithms. This substitution eliminates the need for invasive physical measurements while achieving comparable or superior accuracy in determining elastic properties.
2Reliability
If traditional reservoir modeling methods are used without elastic property constraints, then the modeling process is simpler and faster, but the reliability and accuracy of hydrocarbon removal predictions decrease
Solution Approach 1:
The patent implements feedback by using determined elastic properties to constrain and guide the reservoir modeling process. The elastic properties act as feedback parameters that validate and adjust the model predictions, ensuring that simulated hydrocarbon removal scenarios are consistent with the actual subsurface mechanical behavior. This feedback loop enhances reliability without significantly impacting modeling efficiency.
3Measurement precision
If more data collection methods are employed to improve elastic property determination, then measurement precision increases, but loss of time and increased cost occur
Solution Approach 1:
The patent performs preliminary action by deriving seismic attributes and calculating elastic properties before the main reservoir modeling and hydrocarbon removal planning processes. This preliminary characterization of lithology and elastic properties provides a solid foundation for subsequent decisions, eliminating the need for time-consuming data collection during critical planning phases.
Data Source
AI summary
An information processing system having a processor and a memory device coupled to the processor, wherein the memory device includes a set of instruction that, when executed by the processor, cause the processor to receive a multi-dimensional grid of acoustic or elastic impedances determined from seismic survey data associated with a subterranean formation, receive elastic property data that describes elastic property characteristics used to sort pseudo-components, and wherein the respective pseudo-components are formed of a combination of two or more lithologies. The instructions, when executed by the processor, further cause the processor to define select design variables using the impedance arrays, perform optimization operations for optimizing select design variables by applying the elastic property data as a part of a constitutive relation, and output a distribution of the pseudo-components to characterize volumetric concentrations of spatially grouped lithologies in a control volume of the subterranean formation.