New Oil Formation Volume Factor Correlation for Nigerian Crude Oils

Atthi, A.J. and Sulaimon, A.A. and Akinsete, O.K. (2022) New Oil Formation Volume Factor Correlation for Nigerian Crude Oils. In: UNSPECIFIED.

Full text not available from this repository.
Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

A comprehensive description of reservoir fluid properties is critical in developing solutions and resolving reservoir engineering issues. The oil formation volume factor, βo, is an indispensable reservoir fluid property in reservoir engineering calculations. In this study, we used a total of 11040 data points from 1840 oil samples to develop new βo correlations for the Nigerian crude oils specifically, and another set of correlations for the other regions herein referred to as the global crude oils. Linear regression (LR), multiple linear regression (MLR), multiple non-linear regression (MNLR), neural network (NN), support vector machine (SVM), and the group method of data handling (GMDH) techniques were used to develop several correlations. Results show that the GMDH method yielded the best correlation while the MNLR is the least accurate. The root means square error (RMSE) for the Nigerian, and Global correlations are 0.0033, and 0.0256 respectively. The two correlations are reliably better in terms of accuracy than the existing correlations. The new correlations would facilitate a more accurate reservoir characterization, and reliable design of surface equipment. © 2022, Society of Petroleum Engineers.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: cited By 1; Conference of 2022 SPE Nigeria Annual International Conference and Exhibition, NAIC 2022 ; Conference Date: 1 August 2022 Through 3 August 2022; Conference Code:181613
Uncontrolled Keywords: Crude oil; Data handling; Gasoline; Multiple linear regression; Neural networks; Petroleum reservoir engineering, Fluid property; Group method of data handling; Neural-networks; Nigerians; Oil formation volume factors; PVT properties; Regression; Reservoir engineering; Reservoir fluid; Support vectors machine, Support vector machines
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 19 Dec 2023 03:23
Last Modified: 19 Dec 2023 03:23
URI: https://khub.utp.edu.my/scholars/id/eprint/17556

Actions (login required)

View Item
View Item