Real-time prediction of formation pressure gradient while drilling

et al. 2023

Preprint

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.

Section: General Regression Neural Network (Grnn)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

Preprint

“…Moreover, Ahmed et al (2019), Tanko and Bello (2020) and Abdelaal et al (2022) presented a New ANN for Pore Pressure Prediction While Drilling. In addition, Mahmoud et al (2020) used machine learning techniques such as ANN and Functional neural networks (FNN) to develop models for estimating Static Young's modulus for sandstone formations.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

Preprint

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.

“…Seismic data, well logs, and drilling information are required information for determining the pore pressure gradient in a field. In case of a lack of necessary information in a part of the field after screening the available data and preparing the database, the necessary well logs are prepared using estimating models (Abdelaal et al 2022;Haris et al 2017;Jindal and Biswal 2016;Radwan et al 2020;Radwan 2021). Sonic logs can be a good indicator of the internal pressure of the ground.…”

Section: Introductionmentioning

confidence: 99%

Determining the drilling mud window by integration of geostatistics, intelligent, and conditional programming models in an Oilfield of SW Iran

et al. 2023

Preprint

Accurate knowledge of pore and fracture pressures is essential for drilling wells safely with the desired mud weight (MW). Overpressure occurs when the pore pressure is higher than the normal hydrostatic pressure. There is a challenge regarding the pressure studies domain in an oilfield in SW Iran, where lack of geo-mechanical data limits exact mud window calculation. Also, the reservoir generally consists of carbonate rocks and contains no shale interbeds, so mechanical stratigraphy based on Gamma ray could not be applied. This study is to provide safe drilling considering MW to prevent the flow or loss in the vicinity of the new wells in the studied field. In this research the formation pressures and mud window models are determined by combining geostatistical, intelligent, and conditional programming models and compared with real data. The conditional programming was also used to correct small out-of-range data. The highest correlation between the final effective pressure and velocity cube was observed in lower Fahliyan Formation with 0.86 and Ilam with 0.71.The modeled MW difference ranged between 2.5 and 30 PCF. Also, the maximum modeled MW is 150 PCF in the upper Fahliyan Formation. Heavy mud of more than 130 PCF is suggested for drilling the Khalij member and continues to the end of stratigraphy column. Best observed correlation comparing the drilled and modeled MW, especially achieved in the Fahliyan reservoir Formation with more than 100 PCF and the Ilam Formation with 80-100 PCF. Finally, 3D formation pressures is presented and recommended for further safe drillings.