Multinet Belief Network for Drilling Parameter Optimization
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Solution Overview
Problem
During oilfield operations, interpreting and modeling data from drilling sites is challenging due to varying interpretations by operators and engineers, leading to delayed responses to encountered conditions, which can result in lost production time and profits.
Innovation Solution
The method involves creating a multinet belief network by associating common nodes from two separate belief networks, each representing different sets of decision factors, to generate oilfield parameters with assigned degrees of certainty, allowing for real-time adjustment of drilling operations based on generated parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple separate belief networks are used to model different decision factors, then the comprehensiveness of data analysis is improved, but the complexity of the system increases and response time decreases
Solution Approach 1:
The patent combines multiple separate belief networks into a unified multinet belief network structure. This merging approach maintains the comprehensive analysis capabilities of multiple networks while reducing overall system complexity through shared common nodes and standardized integration protocols.
Solution Approach 2:
The multinet belief network creates a universal framework that can handle multiple types of decision factors and data sources through a common structure. This multi-functional design allows the same network architecture to analyze various drilling parameters, equipment states, and operational conditions without requiring separate specialized systems.
2Adaptability or versatility
If multiple operators and engineers interpret data independently, then diverse perspectives are obtained, but the time required to reach a consensus decision increases
Solution Approach 1:
The belief network system incorporates feedback mechanisms where the interpreted results from multiple decision factors are continuously refined and adjusted. The network learns from the interactions between different data sources and automatically adjusts probabilities and conclusions, reducing the need for lengthy manual consensus-building while preserving diverse analytical perspectives.
Solution Approach 2:
The multinet belief network acts as an intermediary system that synthesizes interpretations from multiple operators and engineers. Rather than requiring direct human-to-human consensus, the network mediates by integrating various perspectives through its probabilistic reasoning framework and producing a unified decision recommendation.
3Measurement precision
If traditional data collection and modeling methods are used, then data accuracy is maintained, but the speed of interpretation and response to downhole conditions decreases
Solution Approach 1:
The belief network structure is pre-configured with established relationships between decision factors, nodes, and probabilistic dependencies. This preliminary setup allows the system to immediately process new data without requiring time-consuming model construction or calibration, enabling rapid interpretation while maintaining accuracy through pre-validated relationships.
Solution Approach 2:
The belief network is designed to be dynamic and adaptive, allowing it to quickly incorporate new data and adjust interpretations in real-time. The probabilistic nature of the network enables it to dynamically update conclusions as new information becomes available, maintaining accuracy while significantly improving interpretation speed compared to static traditional methods.
Data Source
AI summary
A computer usable medium including computer usable program code for determining an oilfield parameter for a drilling operation. The computer usable program code when executed causing a processor to identify first decision factors and second decision factors about the drilling operation, where each of the first decision factors is contained within first nodes, and where each of the second decision factors is contained within second nodes, where the first and second nodes contain common nodes. The computer usable program code further causing the processor to associate the first nodes to create a first belief network and associate the second nodes to create a second belief network, associate the common nodes of the first belief network with the common nodes of the second belief network to form a multinet belief network, and generate at least one oilfield parameter from the multinet belief network.