Ahead of Bit Pore Pressure Prediction using VSP - A Case Study in South China Sea

Xie, Yizhen; Cai, Jianfei; Zhen, Ling Xia; Hong, Tzung-Pei; Li, Yan Hua; Voskamp, Arne; Wu, Bailin

doi:10.2523/130551-ms

Cited by 6 publications

(2 citation statements)

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“…In order to calculate the hydrostatic pressure, the hydrostatic pressure gradient introduced for the Middle East region, especially Iran, was used. The hydrostatic pressure gradient is assumed to be 0.464 (psi/ft; Poursiami 2013; Xie et al 2010).…”

Section: Methodsmentioning

confidence: 99%

ANN Based Estimation of Pore Pressure of Hydrocarbon Reservoirs - A case study

Kianoush

Mohammadi

Hosseini

et al. 2023

Preprint

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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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Section: Methodsmentioning

confidence: 99%

ANN Based Estimation of Pore Pressure of Hydrocarbon Reservoirs - A case study

Kianoush

Mohammadi

Hosseini

et al. 2023

Preprint

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Section: Methodsmentioning

confidence: 99%

Application of Intelligent Models in Determining the Pore Pressure of Hydrocarbon Reservoirs

Kianoush

Mohammadi

Hosseini

et al. 2023

Preprint

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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. Suitable attributes for estimating compressional velocity (Vp) and density from seismic data are extracted by the Emerge module of HRS.8 software. 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. Correlation between 0.2 and 0.3 is suitable for generating a neural network layer, and values below 0.2 have a low correlation. 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 with Petrel 2016 software.

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Worldwide Pore Pressure Prediction: Case Studies and Methods

Tang¹,

Luo

Qiu

et al. 2011

All Days

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The paper presents methods and multiple examples of published case studies of pore pressure prediction in worldwide deepwater exploration. The case studies show that seismic data were mostly used to carry out predrill pore pressure prediction, especially for wildcat frontier areas. It is critical to process seismic data to obtain more reliable fit-for-purpose velocity models and to apply appropriate pore pressure models. Real-time updating with newly acquired data allows the uncertainties of the predrill pore pressure prediction to be reduced ahead of the bit. This paper gives a brief introduction on worldwide deepwater exploration activities, provides a summary of the most common and frequently used pore pressure and fracture gradient methods, and captures best practices on pore pressure prediction for deepwater exploration. Distribution of Worldwide Deepwater Petroleum ExplorationThe easy-to-find found petroleum reservoirs located land and offshore continental shelf have mostly been discovered. The oil and gas industry has now focused on how to exploit, discover, develop, and produce petroleum in remote and increasingly challenging locations such as offshore deep water areas. Deepwater operations of all worldwide discoveries are distributed in Africa, Europe, Latin America, North America, and the Asia Pacific region. AfricaDeepwater operations in Africa are relatively immature because activities in the region began in earnest only in the early 1990s. Most deepwater operations and activities in Africa are currently concentrated in West Africa and North Africa. Angola leads all aspects of deepwater operations in Africa, accounting for 45% of discovered fields and 45% of fields onstream in Africa. The potential for profitable deepwater prospects in Africa is huge, especially in the deepwater and ultra deepwater areas expected to come on-stream between 2008 and 2012. Africa field prospects represent 28% of worldwide field forecasts (Iledare, 2009). EuropeEurope accounted for 7% of deepwater fields discovered in . Approximately 12% of discovered deepwater fields brought on-stream during this period were in Europe (as of end-of-year 2007). The bulk of deepwater activity in 1983-2007 was concentrated in Norway--Nearly 75% of deepwater petroleum reserves discovered in Europe in 1983-2007 were in Norway (Iledare, 2009).

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Ahead of Bit Pore Pressure Prediction using VSP - A Case Study in South China Sea

Cited by 6 publications

References 0 publications

ANN Based Estimation of Pore Pressure of Hydrocarbon Reservoirs - A case study

ANN Based Estimation of Pore Pressure of Hydrocarbon Reservoirs - A case study

Application of Intelligent Models in Determining the Pore Pressure of Hydrocarbon Reservoirs

Worldwide Pore Pressure Prediction: Case Studies and Methods

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