Retrodictive Model for Source Rock Spatial Distribution
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Solution Overview
Problem
Current methods for imaging and predicting the spatial distribution of petroleum source rocks in frontier and underexplored basins are inadequate, as they fail to accurately locate and characterize these rocks, leading to exploration risks due to limited seismic data and lack of optimal well placement.
Innovation Solution
A method and system that utilize palaeogeography data, including palaeotopography, palaeobathymetry, and palaeo-earth systems models, to estimate the spatial distribution of source rocks by calculating a retrodictive model, incorporating gravitational resedimentation and organic matter flux prediction, enabling improved imaging and prediction of source rock distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If marine seismic data acquisition and processing are used to image geophysical structures, then a profile of subsurface layers is generated, but the accurate location of oil and gas reservoirs and source rocks cannot be determined
Solution Approach 1:
The system performs preliminary actions by integrating multiple data sources (seismic data, well data, geological models) before the actual exploration decision-making process. This allows the system to pre-calculate source rock distribution and quality metrics, so when exploration is needed, the information is already available, eliminating the need for exploratory drilling to discover source rocks.
Solution Approach 2:
The system introduces an intermediary computational model that bridges the gap between seismic data and source rock location. This model integrates seismic attributes, geological knowledge, and environmental factors to infer source rock distribution, acting as a mediator that translates indirect seismic signals into actionable exploration information without requiring direct observation.
2Reliability
If exploration wells are drilled in frontier basins, then some data about subsurface structures is obtained, but optimal well placement cannot be determined due to lack of source rock characterization
Solution Approach 1:
The system performs multiple functions within a single integrated platform: it images subsurface structures, characterizes source rocks, predicts hydrocarbon charge, and optimizes well placement. This multi-functional approach increases exploration reliability by providing comprehensive analysis without requiring separate complex systems for each function.
Solution Approach 2:
The system merges previously separate exploration methods (seismic imaging, source rock evaluation, charge assessment) into a unified workflow. By combining these functions into one integrated system, the complexity is managed through standardized interfaces and shared data models, allowing improved reliability without proportionally increasing overall system complexity.
3Measurement precision
If traditional seismic methods are used to characterize source rocks, then some subsurface information is obtained, but quantitative prediction of source rock distribution and quality is not achieved
Solution Approach 1:
The system replaces traditional mechanical seismic interpretation methods with computational modeling and data integration approaches. Instead of relying on seismic attributes alone, the system uses quantitative models that integrate multiple data types to predict source rock properties, achieving both high precision and improved productivity through automated computational analysis.
Solution Approach 2:
The system changes the parameters used for source rock characterization from traditional seismic attributes to a comprehensive set of parameters including organic matter content, thermal maturity, source rock quality indices, and charge potential. This parameter transformation enables quantitative prediction while maintaining computational efficiency through standardized calculation methods.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy of source rock prediction, reducing exploration risks by providing a more detailed and quantitative understanding of source rock distribution, applicable not only to petroleum but also to other Earth resources like diamond and gold deposits.
Implementation Method 1
estimating gravitational resedimentation in deep water based on bathymetric mapping
Data Source
Figure 1
Figure 2A~2B
Figure 3A~3B
AI summary
System and method for estimating a spatial distribution of a characteristic associated with Earth resources. The method includes receiving (1100) at an interface (1408) palaeogeography data including (1) palaeotopography data, (2) palaeobathymetry data, (3) and a palaeo-earth systems model; calculating (1102) with a processor, a retrodictive model of the characteristic based on the (1) palaeotopography data, (2) the palaeobathymetry data, and (3) the palaeo-earth systems model; and imaging (1104) the spatial distribution of the characteristic over a part of the Earth.