Reserves Ranking Analytics for Petroleum Reservoir Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current petroleum reservoir management lacks the ability to effectively evaluate and optimize the productivity and recovery of petroleum reservoirs due to inadequate understanding of geophysical barriers and inefficient resource allocation, leading to low short-term and long-term production and recovery rates.
Innovation Solution
The implementation of Reserves Ranking Analytics (RRA), a systematic methodology that classifies petroleum reservoirs using geotechnical and economic metrics to identify opportunities for exploitation, determine recovery potential, and adjust reservoir operations, such as well management and drilling, to maximize sustainable hydrocarbon output and capital efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional individual well analysis is used to determine productivity, then well-level productivity can be assessed, but aggregate reservoir productivity and resource allocation optimization cannot be evaluated
Solution Approach 1:
The patent combines individual well productivity data with reservoir-level geophysical and operational data to create a comprehensive analytics system. This merging enables simultaneous assessment of both well-level and aggregate reservoir productivity, eliminating the information loss that occurs when only individual wells are analyzed in isolation.
Solution Approach 2:
The analytics system is designed to serve multiple functions: it evaluates individual well productivity, assesses aggregate reservoir performance, identifies geophysical barriers, and optimizes resource allocation across multiple reservoirs. This multi-functional approach allows a single system to address both the precision of well-level analysis and the breadth of reservoir-level evaluation.
2Ease of operation
If resources are allocated based on traditional metrics without geophysical analysis, then resource allocation can be simplified, but recovery rates and production efficiency remain suboptimal
Solution Approach 1:
The system performs preliminary geophysical analysis and reservoir characterization before resource allocation decisions are made. By pre-identifying geophysical barriers, connectivity issues, and productive zones through advanced analytics, the system enables informed resource allocation that optimizes recovery rates while maintaining operational efficiency.
Solution Approach 2:
The patent replaces traditional empirical resource allocation methods with a data-driven analytics system that uses geophysical models, production data, and machine learning algorithms. This substitution transforms resource allocation from a simplified but inefficient process into an optimized decision-making framework that maintains ease of operation through automated analytics while dramatically improving recovery rates.
3Productivity
If geophysical analysis and advanced analytics are implemented across all reservoirs, then recovery potential can be maximized, but operational complexity and data processing requirements increase significantly
Solution Approach 1:
The analytics system is segmented into modular components that can be deployed at different levels: well-level analytics, reservoir-level analytics, and field-level analytics. This segmentation allows organizations to implement geophysical analysis at the scale appropriate to their needs, maximizing recovery potential while managing complexity through a hierarchical, scalable architecture.
Solution Approach 2:
The patent introduces an intermediary analytics platform that sits between raw geophysical data and decision-making processes. This intermediary layer processes complex geophysical data, translates it into actionable insights, and presents simplified recommendations to operators. The intermediary manages system complexity internally while maintaining user-friendly interfaces and streamlined workflows.
4Duration of action of stationary object
If existing wells are maintained without strategic intervention, then operational continuity is preserved, but short-term production and long-term recovery are diminished
Solution Approach 1:
The analytics system continuously monitors well performance, production rates, and geophysical conditions, providing real-time feedback to operators. This feedback enables strategic interventions such as well shutdowns, workovers, or repositioning when analytics indicate declining productivity trends. The feedback loop maintains operational continuity by proactively managing well performance rather than reacting to failures, thereby preserving both production and recovery potential.
Solution Approach 2:
The system enables dynamic well management where operational strategies are continuously adjusted based on real-time analytics. Rather than static maintenance schedules, wells can be dynamically repositioned, their production rates adjusted, or their operational status changed based on evolving reservoir conditions and analytics insights. This dynamic approach preserves operational continuity while optimizing production and recovery throughout the well lifecycle.
Data Source
AI summary
A method of improving operation of a petroleum reservoir using geotechnical and economic analysis includes classifying a petroleum reservoir using reserves ranking analytics (RRA) and then making one or modifications to the operation of the reservoir. RRA classification includes establishing reservoir classification metrics for each of the following categories: 1) resource size; 2) recovery potential; and 3) profitability. The reservoir can be classified based on at least one metric in the profitability classification category, and also based on at least one metric in one or more of the resource size classification category or the recovery potential classification category. Classification of reservoirs according to RRA can aid in reservoir management, planning, and development.