Open AccessWaterflooding efficiency estimation using capacitance-resistance model with non-linear productivity index
Author(s) -
A. A. Abbasov,
AUTHOR_ID,
Eldar M. Abbasov,
Sh. Z. Ismayilov,
A. A. Suleymanov,
AUTHOR_ID,
AUTHOR_ID,
AUTHOR_ID
Publication year - 2021
Publication title -
elmi əsərlər
Language(s) - English
Resource type - Journals
eISSN - 2218-8622
pISSN - 2218-6867
DOI - 10.5510/ogp20210300528
Subject(s) - productivity , index (typography) , capacitive sensing , production (economics) , computer science , resistive touchscreen , capacitance , linear model , function (biology) , linear approximation , mathematical optimization , nonlinear system , simulation , mathematics , physics , electrode , quantum mechanics , machine learning , evolutionary biology , biology , world wide web , economics , computer vision , macroeconomics , operating system
A modified Capacitive Resistive Model (CRM), with a non-linear Productivity Index (PI), has been suggested to evaluate efficiency of waterflooding in heterogeneous reservoirs. CRM model is based on continuity equation between production and injection, and has several additional advantages. The technique does not require sophisticated geological and hydrodynamics numerical simulation modeling, which would require expensive computing time and based on actual hydrodynamic data. The model adequately describes depletion process and can be used for production forecasting and waterflooding mechanism. Capacitance-resistance model with non-linear productivity index has been tested against numerical model as well as actual production data. Additionally, non-linear productivity index based CRM model was compared against the CRM model with linear productivity index function. The calculations algorithm does not require building static geological models and running dynamic simulations, nor it demands extensive computational resources and time, thanks to using production and injection history only, therefore it can be deployed easily. Keywords: waterflooding; capacitance-resistance model; non-linear productivity index; efficiency; monitoring; forecast.