Joint Inversion of Sonic Resistivity Density Data for Formation Properties
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Solution Overview
Problem
Current methods for determining formation properties in oil and gas fields using well log data face challenges in accurately estimating porosity and water saturation due to the inability of resistivity and sonic data to distinguish between oil and gas, and the non-uniqueness of inversion solutions, especially in formations with three fluid phases.
Innovation Solution
A joint inversion framework is developed that combines sonic, resistivity, and density data to estimate porosity, water saturation, oil saturation, gas saturation, and pore aspect ratio, utilizing petrophysical transforms and tool response simulators to refine a formation model based on measured and simulated log data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If resistivity data is used to estimate water saturation, then water saturation can be determined, but the method cannot distinguish between oil and gas phases
Solution Approach 1:
The patent combines resistivity data with density and sonic data in a joint inversion framework. This merging allows the system to leverage the complementary strengths of each data type: resistivity provides water saturation information, while density and sonic data provide sensitivity to gas phases. The combined inversion simultaneously determines water saturation, oil saturation, and gas saturation, resolving the contradiction by adding dimensional information from multiple measurements.
Solution Approach 2:
The joint inversion framework makes the inversion system multi-functional by enabling it to determine multiple formation properties (porosity, water saturation, oil saturation, gas saturation, and pore aspect ratio) simultaneously from a single integrated analysis. This universal approach allows one inversion process to address multiple measurement limitations that would otherwise require separate analyses.
2Measurement precision
If separate inversions are performed for resistivity, density, and sonic data, then each property can be estimated, but the results are not mutually consistent
Solution Approach 1:
The patent merges separate inversion processes into a single joint inversion framework that processes resistivity, density, and sonic data simultaneously. This unified approach ensures mutual consistency of the resulting formation properties by enforcing coherence across all datasets through a common inversion model, eliminating the inconsistencies that arise from separate analyses.
Solution Approach 2:
The joint inversion framework incorporates feedback mechanisms where the inversion results from one data type inform and constrain the interpretation of other data types. The iterative inversion process continuously adjusts the formation property estimates based on the combined information from all measurements, ensuring self-consistency and mutual validation of the results.
3Reliability
If multiphysics inversion is used to integrate sonic and resistivity data, then robust interpretation is achieved, but the method still cannot characterize three-phase fluid systems
Solution Approach 1:
The patent extends the multiphysics inversion framework to achieve universality in handling complex fluid systems. By incorporating density and sonic data with different sensitivities to various fluid phases, the inversion system becomes capable of characterizing three-phase systems (oil, gas, and water) while maintaining the robustness of multiphysics inversion. The framework can adapt to different fluid configurations based on the sensitivity patterns of the input data.
Solution Approach 2:
The patent utilizes parameter changes in the inversion model to accommodate three-phase fluid systems. By introducing additional parameters for oil saturation and gas saturation alongside water saturation, and by adjusting the sensitivity weights of different data types, the inversion framework can accurately characterize complex multiphase conditions. The pore aspect ratio parameter is also optimized to account for variations in fluid distribution within the pore structure.
Data Source
AI summary
A computer-implemented method is provided for determining properties of a formation traversed by a well or wellbore. A formation model describing formation properties at an interval-of-interest within the well or wellbore is derived from measured sonic data, resistivity data, and density data for the interval-of-interest. The formation model is used as input to a plurality of petrophysical transforms and corresponding tool response simulators that derive simulated sonic data, resistivity data, and density data for the interval-of-interest. The measured sonic data, resistivity data, and density data for the interval-of-interest and the simulated sonic data, resistivity data, and density data for the interval-of-interest are used by an inversion process to refine the formation model and determine properties of the formation at the interval-of-interest. In embodiments, properties of the formation may be radial profiles for porosity, water saturation, gas or oil saturation, or pore aspect ratio.