Pre-Stack Seismic Fluid Prediction Method Driven by Multiple Type Facies

Jia, Weihua

doi:10.1109/TGRS.2024.3398619

Pre-Stack Seismic Fluid Prediction Method Driven by Multiple Type Facies

2024-01-01

0

PRE

AI

DOI：10.1109/TGRS.2024.3398619

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Incorporating geological background information into pre-stack seismic inversion methodologies is a pressing concern within the field of seismic exploration. The current approaches to pre-stack seismic inversion often overlook crucial geological contexts and lack constraints derived from geological data. This deficiency motivates our comprehensive investigation into the utilization of various types of facies information to augment pre-stack seismic inversion methodologies. The classification of facies, encompassing seismic facies, lithofacies, and fluid facies, provides valuable geological insights that serve as a priori knowledge. These classifications, delineated by distinct facies boundaries, exhibit correlations with sedimentary structures, stratigraphic layers, and fluid distributions, thereby enhancing the lateral resolution of inversion results. By harnessing multiple type facies classification results capable of encapsulating the geological characteristics of subsurface media, we develop a probabilistic a priori model that integrates multiple type facies information. Subsequently, we formulate a probabilistic seismic inversion method underpinned by multitype facies constraints, specifically tailored for pre-stack seismic inversion. This methodological advancement provides a robust framework for improving the reliability of seismic inversion outcomes. Through rigorous validation procedures involving both model testing and field data analyses, we verify the efficacy and stability of the pre-stack seismic fluid prediction methodology driven by multiple type facies. This validation underscores the credibility of our approach and highlights its potential to enhance the accuracy and reliability of pre-stack seismic inversion results.

Keyword:

Fluids

Probabilistic logic

Bayes methods

Predictive models

Optimization

Machine learning algorithms

Reservoirs

Facies driven

fluid identification

pre-stack inversion

Pre-Stack Seismic Fluid Prediction Method Driven by Multiple Type Facies

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