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| A comprehensive fluid identification method for carbonate reservoirs considering pore shapes: A case study of the fourth member of the Ordovician Majiagou Formation,Ordos Basin |
WANG Yong-Gang1,2( ), YAO Zong-Hui1,2, YANG Qi-Yu3,4, LI Jing-Ye3,4, SONG Wei3,4 |
1. Research Institute of Exploration and Development,Changqing Oilfield Company,PetroChina,Xi'an 710018,China
2. National Engineering Laboratory for Exploration and Development of Low-Permeability Oil & Gas Fields,Xi'an 710018,China
3. State Key Laboratory of Petroleum Resources and Prospecting,Beijing 102249,China
4. College of Geophysics,China University of Petroleum(Beijing),Beijing 102249,China |
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Abstract The dolomite reservoirs of the fourth member of the Majiagou Formation in the Ordos Basin are characterized by thin layers,tightness,complex pore shapes,strong heterogeneity,and relatively weak seismic responses.The mechanisms behind the seismic prediction of the reservoirs remains uncertain, making fluid identification challenging.Traditional geophysical methods based on a single attribute fail to accurately predict fluids.Therefore,by comprehensively accounting for the pore shapes and connectivity,this study proposed a new fluid identification method using petrophysical modeling and analysis,as well as wave theory-based prestack AVO inversion,frequency-dependent AVO inversion of fluid factors,and probabilistic neural network(PNN)-based prediction of pore structure parameters.This method,comprehensively considering the impacts of elastic parameters,physical parameters,and dispersion properties,achieve remarkable results.Compared to traditional single-attribute fluid identification method,the proposed method demonstrates higher accuracy,particularly for gas-bearing areas,fully verifying its effectiveness in fluid identification and highlighting its potential for widespread application.
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Received: 07 May 2024
Published: 26 February 2025
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Well 1 synthetic record
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Flowchart of the integrated fluid factor prediction
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A petrophysical model of carbonate rocks considering connected pores and pore shapes
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Effect analysis of pore parameters on elastic modulus based on rock physical model
a—the relationship between pore parameters and bulk modulus when connected pores change; b—the relationship between pore parameters and shear modulus when connected pores change; c—the relationship between pore parameters and density when connected pores change; d—the relationship between pore parameters and bulk modulus when soft pore content changes; e—the relationship between pore parameters and shear modulus when soft pore content changes; f—the relationship between pore parameters and density when soft pore content changes
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Shear wave velocity prediction results
a—shear wave prediction results of quality control well I1; b—shear wave prediction results of quality control well I2; c—shear wave prediction results of prediction well 1; d—shear wave prediction results of prediction well 2
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Sensitivity analysis of pore shape parameter(soft pore content)
a—scatter-point analysis of P-S velocity ratio and bulk modulus;b—scatter analysis of shear modulus and Poisson's ratio;c—bulk modulus and Poisson's ratio scatter analysis;d—scatter analysis of P-wave velocity and S-wave velocity;e—bulk modulus,p-wave velocity and Poisson's ratio scatter analysis;f—scatterpoint analysis of P-wave velocity,S-wave velocity and P-wave modulus;g—bulk modulus,density and P/S velocity ratio scatter analysis
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Flow chart of PCNN model
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LP-CNN multi-attribute fusion process(using two attributes as an example)
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Model reservoir(a) and gas saturation(b) diagram
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Earthquake forward modeling resul
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Schematic diagram of single-attribute results
a—longitudinal wave velocity difference(normalized result);b—dispersion factor(normalized result);c—pore structure parameters
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Prediction results of multi-attribute fusion(normalized results)
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Schematic of the prediction results of the fourth section of Majiagou Formation
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