Prospect Evaluation System for Oil and Gas Exploration
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Solution Overview
Problem
The oil and gas industry faces challenges in evaluating and ranking prospects for exploration due to the complexity of geological, structural, and seismic investigations, requiring significant data analysis and risk assessment, which can be impacted by new data from seismic acquisitions and drilling results.
Innovation Solution
A system and method for evaluating prospects in oil and gas exploration that involves receiving prospect information, extracting relevant characteristics, identifying similar prospects and exploration results, validating success estimates, and updating them based on inconsistencies, using a cognitive computing system with machine learning and natural language processing to analyze data and provide a success estimate for drilling decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive geological, structural, and seismic investigations are conducted to evaluate prospect risk, then measurement precision of exploration potential is improved, but device complexity and time consumption increase
Solution Approach 1:
The prospect evaluation process is segmented into distinct analytical modules: geological characteristic analysis, structural analysis, seismic data processing, and risk assessment components. Each module independently processes specific aspects of prospect evaluation, allowing comprehensive analysis while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
A computer system acts as an intermediary between raw geological/seismic data and exploration decisions. The system processes, integrates, and analyzes multiple data types (geological surveys, seismic acquisitions, drilling results) to produce synthesized risk assessments, reducing the complexity burden on human evaluators while maintaining high measurement precision.
2Measurement precision
If comprehensive geological, structural, and seismic investigations are conducted to evaluate prospect risk, then measurement precision of exploration potential is improved, but time consumption increases
Solution Approach 1:
The system performs preliminary processing and analysis of geological, structural, and seismic data as they become available, rather than waiting for complete datasets. Early risk assessments are generated based on partial information and refined as additional data arrives, reducing total evaluation time while maintaining accuracy through iterative refinement.
Solution Approach 2:
The evaluation process operates continuously as new data becomes available from seismic acquisitions and drilling operations. The system continuously updates risk assessments rather than performing discrete batch analyses, ensuring that exploration decisions are based on the most current information without requiring complete data sets before beginning evaluation.
3Productivity
If manual analysis of prospect data is performed, then ease of operation is maintained, but productivity decreases
Solution Approach 1:
The system performs self-service by automatically processing geological, structural, and seismic data without requiring manual intervention for each analysis step. The computer system independently executes evaluation algorithms, integrates multiple data sources, and generates risk assessments, significantly improving productivity while presenting simplified interfaces to users for ease of operation.
Solution Approach 2:
Manual mechanical analysis processes are replaced with automated computer-based systems that process geological and seismic data. The system substitutes human analysts' manual work with algorithmic processing, achieving higher productivity while maintaining ease of operation through user-friendly interfaces that require minimal technical expertise.
Data Source
AI summary
A system, method and program product for evaluating a prospect. The method includes: receiving prospect information regarding a prospect for natural resource exploration; extracting a set of characteristics based on the prospect information; identifying a set of similar prospects and a set of prospect exploration results, based on the set of characteristics; receiving a success estimate for the prospect; validating the success estimate based on the set of characteristics, the set of similar prospects, and the set of prospect exploration results to determine inconsistencies in the success estimate; and approving the success estimate or receiving an updated success estimate, based on the inconsistencies in the success estimate.


