Bayesian Decision Network for Well Completion Optimization

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Solution Overview

Problem

Current well completion operations in oil and gas drilling are time-consuming, expensive, and produce inconsistent results due to reliance on traditional techniques that do not incorporate recent practices or changes, lacking efficiency and effectiveness.

Innovation Solution

The implementation of a well completion expert system utilizing Bayesian decision networks (BDN) that provides recommendations for drilling fluids, packers, junction classifications, perforation, and open hole gravel packing based on inputs and Bayesian probability calculations, integrating expert opinions and data for optimized decision-making.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional techniques are used for well completion operations, then field experience and laboratory work can be accumulated, but the process becomes time-consuming and expensive

Engineering Contradiction:

Improveconsistency of resultsVSAvoidtime for well completion operations

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary analysis by building a Bayesian decision network model before actual well completion operations. Historical data, expert opinions, and laboratory results are pre-processed and integrated into the model structure, allowing rapid evaluation of completion options without repeating time-consuming traditional analysis for each new well

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces traditional mechanical expert judgment and manual laboratory evaluation with an automated Bayesian decision network system. The system uses probabilistic graphical models and computational algorithms to substitute human expert analysis, providing consistent, data-driven recommendations that eliminate variability in traditional expert-based decision-making

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If traditional techniques are used for well completion operations, then established practices can be followed, but the operations produce inconsistent results

Engineering Contradiction:

Improveconsistency of resultsVSAvoideffectiveness of drilling processes

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The Bayesian decision network incorporates feedback loops where posterior probabilities from previous well completions are used to update prior probabilities for future decisions. The system continuously learns from new data and expert opinions, refining its recommendations over time. This feedback mechanism ensures consistent application of lessons learned while adapting to new information, resolving the inconsistency problem of traditional methods

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system transforms qualitative expert opinions and heterogeneous data into standardized probabilistic parameters within the Bayesian framework. By converting diverse input types (expert judgments, laboratory results, field data) into uniform probability distributions and conditional probability tables, the system achieves consistent processing of all inputs, eliminating the variability inherent in traditional qualitative assessment methods

Inventive Principle:

Principle #35Parameter changes

3Loss of information

If traditional techniques are used for well completion operations, then comprehensive field experience can be utilized, but the process becomes expensive

Engineering Contradiction:

Improveincorporation of expert opinionsVSAvoidcost of well completion operations

Core Design Contradiction:

Loss of informationVSLoss of energy

Solution Approach 1:

The patent merges multiple information sources including field experience, laboratory work, expert opinions, and operational data into a single integrated Bayesian decision network model. By combining these previously separate and redundant processes into one unified system, the patent eliminates duplicate efforts and reduces overall costs while preserving all valuable information from each source

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The Bayesian decision network serves multiple functions simultaneously: it stores expert knowledge, processes new data, generates recommendations, and provides decision support. This multi-functional system replaces multiple separate traditional processes (literature reviews, expert consultations, laboratory analyses), reducing overall operational costs while maintaining comprehensive information incorporation

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS9382791B2Expert systems for well completion using bayesian decision networks including a fluids damage and temperature effects uncertainty node, a perforation considerations uncertainty node, a perforation analysis uncertainty node, and a perforation type decision node

Publication Date: 2016.07.05 SAUDI ARABIAN OIL CO

AI summary

Systems and methods are provided for expert systems for well completion using Bayesian decision networks to determine well completion recommendations. The well completion expert system includes a well completion Bayesian decision network (BDN) model that receives inputs and outputs recommendations based on Bayesian probability determinations. The well completion BDN model includes a treatment fluids section, a packer section, a junction classification section, a perforation section, a lateral completion section, and an open hole gravel packing section.