Real-Time Pore Pressure Prediction in Depleted Reservoirs using Regression Analysis and Artificial Neural Networks
It is known that pore pressure (Pp) is an integral part for the well planning process. Pore pressure can be directly measured from the wireline pressure and well tests, or indirectly measured from seismic velocities, well logs, and shale densities. While the direct measurements are limited due to the cost and time-saving purposes, the indirect methods are often used, especially the techniques that based on the mechanical compaction of fine-grained sediments. However, the loss of porosity in carbonate reservoirs is not only controlled by the effective stresses, but also affected by a variety of depositional environments and diagenetic processes. Most of the previous models were developed to detect the overpressure zones rather than the subnormal (i.e., depleted) zones. There are also some limitations in the traditional methods, as they are based on empirical relations and constants that can differ from basins to others. This study presents a regression analysis (RA) and artificial neural networks (ANNs) capable of predicting the Pp using measurable well logs.
A field case, located in SE Iraq, has been investigated to determine the Pp from well log data. A database for five offset wells of Mishrif reservoir was subjected to the predictive methods. Two traditional methods, the Eaton and the Ratio methods, were also conducted to compare their performance with in-situ pore pressure data in carbonate reservoirs.
The current results showed that the true vertical depth, bulk density, neutron porosity, gamma ray, compressional travel time, and unconfined compressive strength are the key parameters for the Pp prediction. An empirical model with a good performance using ANNs has been developed to estimate the Pp using petrophysical well logs. Although both RA and ANNs are conservative in predicting Pp, the higher value of determination coefficient (0.96) of ANNs demonstrated that the ANN can predict the subnormal pore pressures in carbonate reservoirs. While the Eaton and the Ratio methods which are based on the drilling derived dc values showed a closer alignment with the in-situ Pp direct measurements, they are not applicable in depleted carbonate reservoirs. Other indicators of the prediction Pp should be used in conjunction with penetration rate. The validity of the proposed models was successfully checked with the data from another field study in SE Iraq. This study presents efficient and cost-effective models to estimate the formation pore pressure in depleted carbonate environments utilizing petrophysical well logs.
F. Hadi et al., "Real-Time Pore Pressure Prediction in Depleted Reservoirs using Regression Analysis and Artificial Neural Networks," Proceedings of the SPE Middle East Oil and Gas Show and Conference (2019, Manama, Bahrain), Society of Petroleum Engineers (SPE), Mar 2019.
SPE Middle East Oil and Gas Show and Conference 2019, MEOS 2019 (2019: Mar. 18-21, Manama, Bahrain)
Geosciences and Geological and Petroleum Engineering
Center for High Performance Computing Research
Keywords and Phrases
Carbonation; Compressive strength; Cost benefit analysis; Cost effectiveness; Forecasting; Gamma rays; Neural networks; Oil wells; Petroleum geology; Petroleum reservoirs; Petrophysics; Pore pressure; Porosity; Regression analysis; Well testing, Depositional environment; Determination coefficients; Fine-grained sediment; Formation pore pressure; Mechanical compaction; Pore pressure prediction; True vertical depth; Unconfined compressive strength, Well logging
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2019 Society of Petroleum Engineers (SPE), All rights reserved.
01 Mar 2019