Crude Blending Forecasting for Target Grade Field Planning
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Solution Overview
Problem
In diverse and complex hydrocarbon portfolios with multi-fields and multi-reservoirs of varying crude quality, field development and production strategies face challenges in achieving desired life cycle crude grades while maintaining target production levels, due to the complexity of blending and optimizing crude oil properties.
Innovation Solution
A computer-implemented Crude Blending, Optimization, and Forecasting (CBO&F) Tool that utilizes graphical user interfaces and mathematical equations to optimize crude blending, track crude quality variation, and forecast blend properties, enabling automated decision-making for achieving target crude grades and maintaining production levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If crude oil from multiple fields with varying crude quality is blended to achieve desired life cycle crude grade, then crude grade consistency is improved, but blending complexity and operational difficulty increase
Solution Approach 1:
The patent replaces manual blending operations with an automated computer-implemented system that performs calculations, optimizations, and forecasts. The system automatically determines blend proportions, predicts crude grade outcomes, and generates blending recommendations, eliminating the need for complex manual calculations and expert judgment in each blending decision.
Solution Approach 2:
The system changes the approach from qualitative blending decisions to quantitative parameter-based control. By using mathematical models to calculate precise blend proportions, predict crude grade properties (API gravity, sulphur content, etc.), and optimize blending parameters, the system achieves consistent crude grade outcomes through controlled parameter adjustment rather than complex operational procedures.
2Productivity
If automated blending optimization is implemented to achieve target crude grade, then decision-making efficiency is improved, but system complexity and initial setup requirements increase
Solution Approach 1:
The system performs preliminary calculations and optimizations before actual blending operations. By pre-calculating blend proportions, predicting crude grade outcomes, and determining optimal blending strategies in advance, the system enables rapid decision-making during operations without requiring complex real-time computational resources or expert intervention.
Solution Approach 2:
The system creates virtual models and simulations of blending scenarios to predict outcomes before actual blending occurs. By using computational models to replicate blending processes and forecast results, the system simplifies decision-making by providing pre-determined recommendations based on simulated performance rather than requiring complex real-time analysis.
3Measurement precision
If comprehensive crude quality forecasting is performed to track quality variation over production life cycle, then planning accuracy is improved, but computational requirements and analysis time increase
Solution Approach 1:
The system performs forecasting calculations in advance during the planning phase rather than requiring real-time analysis during production. By pre-calculating crude grade trajectories and quality variations over the production life cycle using stored historical data and mathematical models, the system provides accurate forecasts without requiring extensive computational time during actual operations.
Solution Approach 2:
The system uses automatically stored historical data and integrated mathematical models to generate forecasts without requiring manual analysis or external computational resources. The forecasting functionality serves itself by utilizing existing data structures, calculation frameworks, and stored production histories to automatically generate accurate quality predictions with minimal user intervention or additional computational overhead.
Data Source
AI summary
A computer-implemented method for providing a crude blending, optimization, and forecasting tool is described. One or more feedstocks or components of a petroleum production network are selected using a graphical user interface of a crude blend engine (CBE). The CBE receives input data for one or more feedstocks or components of a petroleum production network and calculates, using the input data for a blend of fluid from the one or more feedstocks or components, total blend production rate and crude blend properties. The CBE calculates, using the total blend production rate and crude blend properties, an estimation of blend fluid properties and crude grade classification for the blend of fluid. The CBE initiates for display the estimation of blend fluid properties and crude grade classification on a computer display graphical user interface. The CBE initiates a blending operation to achieve a target crude grade classification.