Gamma Ray LWD Curve Optimization for Deep-Field Precise Navigation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Deep precise navigation in oil and gas exploration faces challenges in measurement accuracy due to non-formation factors and noise influences, leading to dimensional differences in logging curves and depth errors during drilling.
Innovation Solution
A method for gamma-ray (GR) logging-while-drilling (LWD) curve optimization involving real-time data acquisition, outlier processing, normalization, waveform indication inversion, variance attribute feature extraction, and depth transformation using control points to correct drilling depth and improve reservoir prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time GR LWD data is acquired during drilling, then drilling efficiency is improved, but measurement accuracy deteriorates due to non-formation factors and noise influences
Solution Approach 1:
The patent extracts and removes non-formation factors (outliers) from the GR LWD data through outlier processing. By identifying and eliminating data points caused by drilling fluid, instrument interference, and other non-formation influences, the system preserves the genuine formation signal while maintaining real-time data acquisition capabilities.
Solution Approach 2:
The patent introduces an intermediary normalization process that transforms the raw GR LWD data into normalized values. This intermediary step bridges the gap between real-time acquisition and accurate measurement by standardizing the data against historical curves, thereby eliminating dimensional differences and reducing noise influences.
2Loss of time
If GR LWD data is processed in real-time, then response time is improved, but data quality deteriorates due to dimensional differences and depth errors
Solution Approach 1:
The patent performs preliminary outlier processing and normalization on the real-time GR LWD data before further analysis. By pre-processing the data to eliminate outliers and standardize dimensions, the system prepares the data for accurate depth transformation and reservoir prediction without compromising real-time response.
Solution Approach 2:
The patent employs feedback mechanisms through rationality determination that compares normalized real-time data against historical curves. This feedback loop continuously monitors data quality and adjusts processing parameters to maintain both real-time performance and measurement accuracy, correcting depth errors through iterative optimization.
3Measurement precision
If outlier processing is applied to eliminate noise, then data purity is improved, but processing complexity increases
Solution Approach 1:
The patent changes processing parameters dynamically based on data characteristics. By adjusting outlier detection thresholds and normalization parameters according to the specific drilling conditions and historical curve patterns, the system achieves high data purity while controlling processing complexity through adaptive parameter tuning rather than fixed complex algorithms.
Data Source
AI summary
The present invention is in the field of geological exploration and particularly relates to a method and system for gamma ray (GR) while drilling parameter data optimization for deep-filed oil and gas precise navigation, aiming to solve the problem of insufficient measurement accuracy of the depth of a well in the existing drilling technology. The present invention comprises: eliminating outliers from the obtained GR while drilling parameter data and determining the rationality of the eliminated outlier; performing derivation on a real-time updated variance attribute while drilling curve to obtain a real-time updated formation change-point detection result; searching the formation change-point detection result of a pre-drilling predicted well curve; and correcting the drilling depth by comparing the two formation change-point detection results to obtain the accurate depth while drilling.