Probabilistic CSEM Classification for Hydrocarbon Reservoir Detection
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Solution Overview
Problem
Conventional controlled source electromagnetic (CSEM) surveys face challenges in unambiguously detecting hydrocarbon-bearing intervals due to low vertical resolution and sensitivity to resistivities averaged over large subsurface volumes, which are affected by multiple rock properties, leading to inherent ambiguity in interpreting resistivity data.
Innovation Solution
A method that uses a probabilistic approach to classify subsurface regions by defining mutually exclusive and exhaustive categories based on rock and fluid properties, estimating probability densities, and applying Bayes' Rule to combine prior probabilities with geophysical data, allowing for the prediction of hydrocarbon production potential despite limited information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional CSEM surveys are used to detect hydrocarbon reservoirs, then the survey can identify regions with anomalously high resistivity, but the method cannot unambiguously detect individual hydrocarbon-bearing intervals due to low vertical resolution and averaging effects
Solution Approach 1:
The patent applies segmentation by dividing the subsurface into discrete geological layers and modeling the electromagnetic response for each layer separately. This allows the method to resolve individual hydrocarbon-bearing intervals within a stacked reservoir system, overcoming the averaging effect that causes ambiguity in conventional CSEM surveys. The segmentation of the subsurface model enables precise identification of which specific layers contribute to the observed resistivity anomaly.
Solution Approach 2:
The patent introduces a new dimension of analysis by incorporating prior geological information and constraints into the inversion process. This additional dimension of geological context allows the method to distinguish between different geological scenarios that produce similar resistivity signatures, thereby reducing ambiguity and improving reliable hydrocarbon detection despite limited vertical resolution.
2Volume of stationary object
If CSEM-derived resistivities are used to identify hydrocarbon-bearing intervals, then the method provides resistivity measurements over large subsurface volumes, but resistivity is affected by multiple rock properties making interpretation ambiguous
Solution Approach 1:
The patent uses prior geological information and expert knowledge as an intermediary to bridge the gap between CSEM resistivity measurements and specific rock property identification. This intermediary layer of geological constraints allows the method to interpret resistivity data in the context of known geological settings, reducing the loss of information about specific rock properties while maintaining the ability to survey large subsurface volumes.
Solution Approach 2:
The patent changes the interpretation parameters by moving from absolute resistivity values to relative resistivity anomalies contextualized within specific geological models. This parameter transformation allows the method to maintain sensitivity to hydrocarbon-bearing zones while accounting for the influence of multiple rock properties, thereby reducing interpretational ambiguity without sacrificing the broad survey coverage capability.
3Reliability
If a high threshold for anomalous resistivity is set to avoid false positives from low porosity rocks, then false positive detections are reduced, but hydrocarbon-bearing rocks with significant formation water may be missed
Solution Approach 1:
The patent applies dynamics by making the resistivity threshold adaptive rather than fixed. The method dynamically adjusts the anomaly threshold based on the specific geological context, rock properties, and survey conditions for each prospect. This dynamic thresholding allows the method to maintain high reliability by avoiding false positives from low porosity rocks while simultaneously detecting hydrocarbon-bearing rocks with significant formation water that would be missed by a static high threshold.
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 method effectively classifies potential hydrocarbon reservoirs by transforming two-component resistivity values into estimates of reservoir resistivity, even when layers are below seismic resolution, reducing ambiguity and improving the accuracy of hydrocarbon potential predictions.
Implementation Method 1
Controlled source electromagnetic (CSEM) surveys for mapping subsurface resistivity
Data Source
AI summary
A probabilistic method for classifying observed CSEM response for a resistive anomaly to classify the response into multiple geologic categories indicative of hydrocarbon production potential. Each category is assigned a prior probability (301). For each category, conditional joint probability distributions for observed CSEM data in the anomaly region are constructed (303) from rock property probability distributions (302) and a quantitative relationship between rock/fluid properties and the CSEM data (304). Then, the joint probability distributions and prior probabilities for each category (305) are combined with observed data (307) and used in Bayes' Rule (306) to update the prior category probabilities (308). Seismic data may be used to supplement CSEM data in the method.