Bottom Hole Assembly and Drilling Fluid Selection by Historical Similarity
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Solution Overview
Problem
Existing drilling operations lack efficient methods for selecting and optimizing bottom hole assemblies and drilling fluids based on historical feature data, leading to suboptimal drilling performance and resource extraction efficiency.
Innovation Solution
A method and system that utilize historical feature data to compute feature distances and perform assessments for similarity between drilling operations, providing recommendations for selecting components of the bottom hole assembly and drilling fluids.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional drilling operation methods are used without historical data analysis, then the drilling process is simpler and faster to initiate, but the selection of bottom hole assembly and drilling fluid is suboptimal leading to reduced drilling efficiency
Solution Approach 1:
The system performs preliminary analysis of historical drilling data before the actual drilling operation begins. By pre-processing historical feature data and computing feature distances in advance, the system prepares optimization recommendations that can be directly applied during drilling, improving efficiency without adding operational complexity during the drilling process itself
Solution Approach 2:
The computing system acts as an intermediary between historical drilling data and current drilling operations. It processes historical feature data through feature distance computation and similarity assessment to generate recommendations for bottom hole assembly and drilling fluid selection, bridging the gap between past experience and present decision-making
2Measurement precision
If historical feature data is analyzed using feature distance computation, then the selection accuracy of drilling equipment and fluids is improved, but the computational processing time and complexity increase
Solution Approach 1:
The system computes feature distances for only the most relevant historical drilling operations rather than analyzing all available data. By selectively processing a subset of historical features that are most similar to current conditions, the system achieves high selection accuracy without the computational burden of complete historical dataset analysis
Solution Approach 2:
The system transforms historical drilling data into standardized feature vectors and computes distances based on key parameters such as formation type, drilling depth, and fluid properties. By changing the representation of historical data into comparable feature spaces, the system enables efficient similarity assessment and accurate recommendations with reduced computational overhead
Data Source
AI summary
A method can include receiving input for a drilling operation that utilizes a bottom hole assembly and drilling fluid; generating a set of offset drilling operations using historical feature data, where the historical feature data are processed by computing feature distances; performing an assessment of the offset drilling operations as characterized by at least feature distance-based similarity between the drilling operation and the offset drilling operations; and outputting at least one recommendation for selection of one or more of a component of the bottom hole assembly and the drilling fluid based on the assessment.