Reservoir Characterisation of High-Pressure, High-Temperature Zone of Malay Basin Using Seismic Inversion and Artificial Neural Network Approach

Abstract

The growing demand for hydrocarbons has driven the exploration of riskier prospects in depths, pressures, and temperatures. Substantial volumes of hydrocarbons lie within deep formations, classified as high pressure, high temperature (HPHT) zone. This study aims to delineate hydrocarbon potential in the HPHT zone of the Malay Basin through the integrated application of rock physics analysis, pre-stack seismic inversion, and artificial neural network (ANN). The zones of interest lie within Sepat Field, located offshore Peninsular Malaysia, focusing on the HPHT area in Group H. The rock physics technique involves the cross-plotting of rock properties, which helps to differentiate the lithology of sand and shale and discriminates the fluid into water and hydrocarbon. The P-impedance, S-impedance, Vp/Vs ratio, density, scaled inverse quality factor of P (SQp), and scaled inverse quality factor of S (SQs) volumes are generated from pre-stack seismic inversion of 3D seismic data. The obtained volumes demonstrate spatial variations of values within the zone of interest, indicating hydrocarbon accumulation. Furthermore, the ANN model is successfully trained, tested, and validated using 3D elastic properties as input, to predict porosity volume. Finally, the trained neural network is applied to the entire reservoir volume to attain a 3D porosity model. The results reveal that rock physics study, pre-stack seismic inversion, and ANN approach helps to recognize reservoir rock and fluids in the HPHT zone.

Multi-attribute Probabilistic Neural Network Reservoir Prediction Based on Mudstone Acoustic Time Difference Decompaction Correction

“…The theoretical basis is that the petrophysical characteristics of the reservoir are significantly different from the surrounding rocks (Ashraf et al, 2019;Anees et al, 2022aAnees et al, , 2022b. The acoustic time difference corrected by mudstone decompaction combined with the multi-attribute probabilistic neural network can realize the accuracy inversion of underground reservoir characteristics by continuously learning from abundant sample data and then fitting complex nonlinear functions (Masters, 1994;Xie et al, 2015;Yazmyradova et al, 2021). After decompressing correction, the overlapping area of the acoustic time difference between the reservoir and surrounding rock is reduced, and the neural network seismic inversion based on this has high resolution.…”

A New Perspective of Post-Weld Baking Effect on Al-Steel Resistance Spot Weld Properties through Machine Learning and Finite Element Modeling

“…A machine learning based approach, having a unique advantage to solve complex problems which have no certain explicit laws, lends itself as a suitable method to explore the underlying process-structure-property relationships governing the RSW. ML techniques have been leveraged to develop optimized systems and effective decision making in many engineering and manufacturing fields [7][8][9][10]. Recently, ML algorithms have been employed to address the key issues associated with materials joining, such as the weld nugget prediction based on infrared images using convolutional neural network [11], weld penetration detection from multisource sensing images using ensembled neural network models [12], defect-welding process correlation establishment using decision tree and Bayesian neural network [13], process-property relationship for Al-steel ultrasonic welds using feed forward neural network [14], weld quality monitoring by analyzing in situ signals using multi-layer perception and support vector regression [15], and autonomous nondestructive evaluation of weld quality using convolutional neural network [16,17], etc.…”