[go: up one dir, main page]

de Holanda et al., 2018 - Google Patents

A generalized framework for Capacitance Resistance Models and a comparison with streamline allocation factors

de Holanda et al., 2018

Document ID
12512988606554127609
Author
de Holanda R
Gildin E
Jensen J
Publication year
Publication venue
Journal of Petroleum Science and Engineering

External Links

Snippet

Abstract The Capacitance Resistance Model (CRM) is a fast way for modeling and simulating gas and waterflood recovery processes, making it a useful tool for improving real- time flood management and reservoir analysis. The CRM is a material balance-based model …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design
    • G06F17/5009Computer-aided design using simulation

Similar Documents

Publication Publication Date Title
de Holanda et al. A generalized framework for Capacitance Resistance Models and a comparison with streamline allocation factors
US10909281B2 (en) History matching of hydrocarbon production from heterogenous reservoirs
US20190138672A1 (en) System and method for characterizing uncertainty in subterranean reservoir fracture networks
CN112036098A (en) Method for simulating hydraulic fracture propagation numerical value of deep oil and gas reservoir
US9619592B2 (en) Analysis of enhanced oil recovery (EOR) processes for naturally-fractured reservoirs
Li et al. INSIM-BHP: A physics-based data-driven reservoir model for history matching and forecasting with bottomhole pressure and production rate data under waterflooding
CN112360411B (en) Local well pattern water injection development optimization method based on graph neural network
US20230266502A1 (en) Collaborative optimization method for gas injection huff-n-puff parameters in tight oil reservoirs
CN109033504B (en) Oil-water well casing damage prediction method
CN103380424A (en) System and method for using an artificial neural network to simulate pipe hydraulics in a reservoir simulator
WO2017031857A1 (en) Device for constructing two-cavity salt cavern reservoir ground subsidence prediction model
CN111027249B (en) Machine learning-based inter-well connectivity evaluation method
CN110378004B (en) A Calibration Method for Microseismic Interpretation of Fracturing Fracture Parameter Results
CN109408855B (en) Method and device for calculating fracture conductivity value of horizontal well to be fractured
CA2729107C (en) Generating an estimation of incremental recovery from selected enhanced oil recovery process
CN110717270A (en) Oil and gas reservoir simulation method based on data
Jin et al. 4D Seismic history matching using information from the flooded zone
CN114066666B (en) A method for analyzing well connectivity through injection-production profile monitoring data
CN107644110A (en) A method for evaluating the degree of water flooding of horizontal wells
US20130332132A1 (en) Computerized method for the estimation of a value for at least a parameter of a hydrocarbon-producing region, for planning the operation and operating the region
Nakayasu et al. Evaluating the value of single-point data in heterogeneous reservoirs with the expectation-maximization algorithm
CN116796237A (en) Oil-water well communication relation identification method based on improved graph neural network
Daly et al. Characterisation and Modelling of Fractured Reservoirs–Static Model
Urbancic et al. The Potential for Predicting Production by Characterizing Fluid Flow and Drainage Patterns Using Microseismicity
CN103400189B (en) Software human-hour estimating method based on BP network