From data conditioning, depth imaging and reservoir characterization to machine learning

In seismic methods, pore pressure is estimated by converting seismic velocity into pore pressure and calibrating it with pressure results during the well-testing program. This study has been carried out using post-stack seismic data and sonic and density log data of 6 wells in one of the fields in SW Iran. While an optimum number of attributes is selected, the General regression (GRNN) provides higher accuracy than Back Propagation (BPNN) at the initial prediction stages. However, Acoustic Impedance (AI) is the most applicable seismic attribute used as root and reverses AI for estimating P-wave and density. Using a set of attributes can train the system to estimate the property. The correlation coefficient of actual and predicted P-wave using an AI seismic attribute has been calculated as 0.74 and the multi-attribute technique as 0.79. Also, density and three attributes reach from 0.57 to 0.60, which shows a better relationship between seismic attributes and density. After determining optimum layers with the principal components analysis (PCA), formation pressure was modeled with the feed forward-backpropagation (FFBP-ANN) method.Five information layers, including gamma, Vp, AI, density, and overburden pressure, have the most linear convergence with the initial pressure model and are used to modify the ANN model of effective pressure.

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“…Instead, the radius of the clusters (or expansion) must be determined before training begins (Khatibi and Aghajanpour 2020;Veeken et al 2020).…”

Section: Methodsmentioning

confidence: 99%

ANN-based estimation of pore pressure of hydrocarbon reservoirs—a case study

et al. 2023

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“…These rock properties can be used to identify hydrocarbon and reservoir. Seismic inversion removes the imprints of the wavelet in the seismic data by deconvolution and then by converting the result into impedance (Lindseth, 1979;Veeken et al, 2020;Veeken & Da Silva, 2004). Seismic inversion workflow and mechanism are shown in Fig.…”

Section: Seismic Inversionmentioning

confidence: 99%