Soil Genomic Analysis for Hydrocarbon Field Delineation
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Solution Overview
Problem
The existing methods for identifying hydrocarbon reservoirs, such as drilling delineation wells, are costly and time-consuming, and the use of bacterial communities as indicators is not reliable due to overlapping communities in different locations.
Innovation Solution
A method and system using genomic data analysis of soil samples to identify proteins involved in hydrocarbon metabolization, such as cytochrome P450s, alkane hydroxylase, and flavin-binding monooxygenase, combined with artificial intelligence algorithms to predict hydrocarbon-bearing sites.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If drilling delineation wells is performed to identify hydrocarbon reservoirs, then the accuracy of reservoir delineation is improved, but the cost and time consumption increase significantly
Solution Approach 1:
The patent replaces the mechanical drilling system with a genomic analysis system. Instead of physically drilling wells to sample subsurface materials, the system uses soil surface samples combined with genomic sequencing and machine learning algorithms to predict hydrocarbon presence, thereby eliminating the time-consuming drilling process while maintaining delineation accuracy
Solution Approach 2:
The patent creates a digital copy of the subsurface hydrocarbon distribution by analyzing surface soil genomes. The machine learning model generates a predictive map that replicates the information obtainable from physical well drilling, allowing virtual exploration of the subsurface without actual drilling operations
2Measurement precision
If drilling delineation wells is performed to identify hydrocarbon reservoirs, then the accuracy of reservoir delineation is improved, but the cost increases significantly
Solution Approach 1:
The patent replaces the resource-intensive drilling operation with a laboratory-based genomic analysis system. By using soil surface samples and high-throughput sequencing, the system reduces material consumption and operational costs while achieving the same delineation accuracy through computational analysis rather than physical extraction
Solution Approach 2:
The system creates a digital model of hydrocarbon distribution by analyzing surface soil genomes, replacing the need for physical well drilling. This virtual copying approach eliminates the costly infrastructure requirements and material consumption associated with actual drilling operations
3Ease of operation
If bacterial communities are used as indicators to identify hydrocarbon fields, then the method is simpler than drilling, but the reliability decreases due to overlapping communities in different locations
Solution Approach 1:
The patent changes the detection parameter from general bacterial community composition to specific functional gene sequences (e.g., hydrocarbon metabolization genes). By focusing on functional capabilities rather than taxonomic presence, the system achieves higher reliability in hydrocarbon detection while maintaining operational simplicity through automated genomic analysis
Solution Approach 2:
The patent extracts and analyzes specific functional genes related to hydrocarbon metabolization from the complex bacterial community DNA. By isolating and sequencing only the relevant functional elements rather than analyzing entire communities, the system achieves more reliable hydrocarbon indicators while keeping the methodology simple and automated
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
Provides a cost-effective and resource-efficient approach to identify hydrocarbon reservoirs by analyzing microbial communities' genomic data, reducing the need for costly drilling and enhancing the accuracy of hydrocarbon field delineation.
Implementation Method 1
performing a genetic analysis on the one or more soil samples to obtain genome sequence data
Implementation Method 2
Protein sequences corresponding to the determined genes are obtained and used to determine presence of one or more proteins involved in hydrocarbon metabolization
Implementation Method 3
the one or more artificial intelligence algorithms are selected from the group consisting of artificial neural network (ANN), logistic regression, support vector machine, naïve Bayesian classifier, Bayesian inference, adaptive boosting, decision tree learning, random forest, decision-making, K-means clustering, clustering analysis, and linear regression
Data Source
AI summary
Described is a method for identifying hydrocarbon fields using genomic data. Soil samples are obtained from a geographic site, and genetic analysis is performed on the soil samples to obtain genome sequence data. Gene detection is performed on the genome sequence data to determine genes present in the soil samples. Protein sequences corresponding to the determined genes are determined and used to determine the presence of proteins involved in hydrocarbon metabolization in the soil samples.