Trace Element Microseepage Detection for Oil Field Exploration
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Solution Overview
Problem
Conventional methods for hydrocarbon exploration are lengthy, costly, and limited to advanced stages, lacking effective tools for early detection of vertical hydrocarbon migration and accumulation in surface soil samples.
Innovation Solution
A method combining geochemical analysis with machine-learning techniques to detect vertical microseepage by performing sequential extraction of trace elements and heavy metals from surface soil samples, visualizing results in isoline maps, and applying statistical clustering to identify concentration anomalies, correlating with proven and dry fields for exploration recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional geophysical and organic geochemical methods are used for hydrocarbon exploration, then detection capability is provided, but the process becomes lengthy and costly
Solution Approach 1:
The invention extracts and focuses specifically on inorganic trace element analysis from soil samples, separating this approach from conventional organic geochemical methods and geophysical techniques. By isolating the trace element analysis component and developing dedicated extraction protocols, the method achieves faster, more cost-effective exploration while maintaining detection reliability
Solution Approach 2:
The invention replaces complex geophysical equipment and organic geochemical analysis systems with a simplified trace element analysis approach using standard laboratory instruments. This substitution eliminates the need for expensive field equipment while achieving comparable or superior detection capabilities through chemical analysis of inorganic elements
2Reliability
If conventional exploration methods are used, then detection is possible, but cost increases significantly
Solution Approach 1:
The invention uses readily available, inexpensive laboratory equipment for trace element analysis instead of expensive specialized geophysical instruments. The method employs common chemical reagents and standard analytical techniques that are already present in most laboratories, dramatically reducing exploration costs while maintaining reliable detection capabilities
Solution Approach 2:
By extracting and focusing solely on inorganic trace element analysis, the invention eliminates the need for costly organic geochemical analysis and expensive geophysical surveys. This targeted approach uses minimal resources while providing reliable detection of hydrocarbon seepage
3Measurement precision
If comprehensive geochemical analysis is performed, then accuracy improves, but process complexity increases
Solution Approach 1:
The invention focuses analysis on specific inorganic trace elements that are most indicative of hydrocarbon seepage, rather than performing comprehensive analysis of all possible geochemical parameters. This targeted approach maintains high detection accuracy by concentrating on key indicators while simplifying the overall analytical process
Solution Approach 2:
The invention divides the exploration process into distinct stages: sample collection, trace element extraction, analysis, and interpretation. This segmentation allows each step to be optimized independently and simplifies the overall methodology by breaking down the complex analysis into manageable, sequential operations
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
Enhances hydrocarbon exploration by providing a cost-effective, early detection of oil reserves through precise geochemical and machine-learning methods, improving the accuracy and efficiency of exploration assessments.
Implementation Method 1
performing sequential extraction of trace elements (TE) and/or heavy metals (HM) from soil samples
Data Source
AI summary
A method for determining exploration potential of a prospect oil field includes determining microseepage at the prospect oil field by performing sequential extraction of trace elements (TE) and/or heavy metals (HM) from soil samples obtained at substantially surface level of the prospect oil field and dry and proven fields, visualizing results of the sequential extraction in isoline maps, using statistical clustering based on a machine learning model to detect concentration anomalies of the extracted trace elements (TE) and/or heavy metals (HM) in the isoline maps of the visualized results, correlating detected concentration anomalies among the prospect, dry and proven fields, and recommending for or against exploration of the prospect oil field depending on whether the concentration anomalies of the prospect field correlate with those of the proven field or the dry field.


