Multi-Layer DTBB Inversion for Reliable Geosteering
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Solution Overview
Problem
Conventional geosteering inversion techniques for directional drilling in hydrocarbon exploration are limited by their reliance on pre-selected formation layer numbers, which can lead to inaccurate models and local minima issues, especially in formations with multiple layers, introducing uncertainty and inefficiency due to the deterministic approach used.
Innovation Solution
A computer-implemented method and system for multi-layer distance to bed boundary (DTBB) inversion that generates multiple initial models with varying formation layer numbers, selects qualified models based on actual measurements and error tolerance, and refines them during operations to achieve global convergence and improved formation evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional deterministic inversion techniques are used with a pre-selected layer number, then the inversion process is computationally efficient, but the model accuracy deteriorates due to incorrect assumptions about the actual number of formation layers
Solution Approach 1:
The patent transforms the static, deterministic inversion approach into a dynamic, adaptive process by implementing multiple inversion runs with varying layer numbers. The system dynamically adjusts the number of formation layers based on actual measurement data, allowing the model to evolve from initial assumptions to accurate representations of the true formation structure.
Solution Approach 2:
The patent systematically varies the layer number parameter across multiple inversion attempts. By changing this critical parameter and evaluating results against actual measurements, the system identifies the optimal layer configuration that best represents the formation, thereby resolving the contradiction between computational efficiency and model accuracy.
2Area of stationary object
If the number of formation layers is increased to represent deep measurements over an extended range, then the model coverage is improved, but the inversion reliability deteriorates due to increased tendency to trap into local minima
Solution Approach 1:
The patent segments the inversion process into multiple independent runs, each with a different layer number configuration. This segmentation allows the system to explore various structural possibilities without being trapped by the complexities of a single high-layer model, thereby maintaining reliability while achieving comprehensive depth coverage.
Solution Approach 2:
The patent employs partial action by performing multiple inversion runs with different layer numbers rather than attempting to solve the complete high-layer problem in a single deterministic run. This approach sacrifices some computational directness but significantly improves reliability by avoiding local minima traps.
3Adaptability or versatility
If gradient-free stochastic inversion techniques are used to optimize the layer number, then the model adaptability is improved, but the productivity deteriorates due to lengthy simulation times and additional computing resources
Solution Approach 1:
The patent implements a dynamic, iterative inversion process that adapts the layer number based on measurement quality and fit criteria. Rather than using computationally intensive stochastic optimization, the system dynamically selects appropriate layer numbers through multiple targeted inversion runs, achieving adaptability with improved computational efficiency.
Solution Approach 2:
The patent incorporates feedback mechanisms where inversion results are evaluated against actual measurements, and the layer number is adjusted based on this feedback. This feedback-driven approach enables the system to adapt to the true formation structure without requiring the extensive computational resources of gradient-free stochastic methods.
4Device complexity
If a pre-selected layer number is used based on a priori information, then the inversion process is simplified and computationally efficient, but the measurement precision deteriorates when the pre-selected number deviates from the actual layer number
Solution Approach 1:
The patent transforms the static pre-selected layer number approach into a dynamic process where the layer number is determined through multiple inversion runs with varying configurations. This dynamic adaptation maintains relative simplicity while significantly improving measurement precision by matching the actual formation structure.
Solution Approach 2:
The patent performs preliminary inversion runs with different layer numbers to identify the optimal configuration before final parameter estimation. This preliminary action allows the system to establish the correct structural framework, ensuring subsequent measurements achieve maximum precision without excessive overall complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables more efficient and accurate geosteering by selecting appropriate inversion models that match actual formation responses, reducing uncertainty and improving drilling efficiency by adjusting the wellbore path based on refined formation property approximations.
Implementation Method 1
The transmitting antenna is used to generate electromagnetic fields in the surrounding formation. In turn, the electromagnetic fields in the formation induce a voltage in each receiving antenna.
Data Source
Figure 1A
Figure 1B
Figure 2
AI summary
System and methods for geosteering inversion are provided. A downhole tool's response along a path of a wellbore to be drilled through a formation is predicted over different stages of a downhole operation, based on each of a plurality of initial models of the formation. Each initial model represents a different number of formation layers over a specified range. The tool's actual response with respect to one or more formation parameters is determined, based on measurements obtained during a current stage of the operation. The actual response is compared with that predicted from each of the initial models. At least one of the models is selected as an inversion model, based on the comparison and a selection criterion. Inversion is performed for subsequent stages of the operation along the wellbore path, based on the selected model. The wellbore path is adjusted for the subsequent stages, based on the inversion results.