Rig KPI Weighting for Holistic Efficiency Evaluation
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Solution Overview
Problem
Current methodologies for evaluating rig performance in the petroleum industry focus solely on mechanical efficiency, neglecting critical non-mechanical factors such as health-safety-environment and local labor performance, which are essential for comprehensive evaluation.
Innovation Solution
A system and method that processes key performance indicator (KPI) data, including flat time, rig lost time, health-safety-environment, and local labor data, to calculate a rig efficiency index (REI), which generates a control signal to adjust the operation state of underperforming rigs, such as re-bidding, re-contracting, releasing, or shutting down, based on predetermined thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If rig performance evaluation focuses solely on mechanical efficiency, then mechanical performance measurement is simplified, but comprehensive performance assessment deteriorates by neglecting health-safety-environment and local labor factors
Solution Approach 1:
The patent combines multiple previously separate evaluation dimensions (mechanical efficiency, health-safety-environment, local labor performance) into a unified rig efficiency index. This merging allows comprehensive performance assessment while maintaining measurement simplicity through automated data integration and standardized weighting schemes.
Solution Approach 2:
The rig efficiency index serves multiple functions simultaneously: it evaluates mechanical performance, incorporates health-safety-environment metrics, accounts for local labor contributions, and provides a single comparable metric across different rig operations. This multi-functional index resolves the contradiction by making comprehensive assessment as useful as simple measurement.
2Measurement precision
If multiple KPI data types are collected and processed to determine comprehensive KPI scores, then performance evaluation comprehensiveness is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex evaluation process into distinct, manageable components: data collection modules for different KPI types, scoring sub-systems for each performance dimension, and a final aggregation layer that combines results. This segmentation allows comprehensive measurement while keeping each component simple and well-defined.
Solution Approach 2:
The system transforms multiple complex KPI datasets into standardized score parameters through normalization and weighting. By changing the parameters from raw diverse data to unified scored metrics, the system achieves precise multi-dimensional evaluation while simplifying the complexity through parameter transformation and aggregation.
Data Source
AI summary
A system and method control at least one rig by receiving key performance indicator (KPI) data of the at least one rig among a set of rigs, including at least one of flat time performance data, rig lost time performance data, health-safety-environment data, and local labor data. The KPI data is processed to determine at least one of a flat time score, a rig lost time score, a health-safety-environment score, and a local labor score as KPI scores. A rig efficiency index (REI) is determined from a weighting of the KPI scores. A control signal based on the REI is determined to control a first rig among the set of rigs.