pywaterflood: Waterflood Connectivity Analysis

pywaterflood provides tools for capacitance resistance modeling, a physics-inspired model for estimating waterflood performance. It estimates the connectivities and time decays between injectors and producers.

Overview

A literature review has been written by Holanda, Gildin, Jensen, Lake and Kabir, entitled “A State-of-the-Art Literature Review on Capacitance Resistance Models for Reservoir Characterization and Performance Forecasting.” They describe CRM as the following:

The Capacitance Resistance Model (CRM) is a fast way for modeling and simulating gas and waterflooding recovery processes, making it a useful tool for improving flood management in real-time. CRM is an input-output and material balance-based model, and requires only injection and production history, which are the most readily available data gathered throughout the production life of a reservoir.

There are several CRM versions (see Holanda et al., 2018). Through passing different parameters when creating the CRM instance, you can choose between CRMIP, where a unique time constant is used for each injector-producer pair, and CRMP, where a unique time constant is used for each producer. CRMIP is more reliable given sufficient data. With CRMP, you can reduce the number of unknowns, which is useful if available production data is limited.

Getting started

You can install this package from PyPI with the line

pip install pywaterflood

A simple example

import pandas as pd
from pywaterflood import CRM

gh_url = "https://raw.githubusercontent.com/frank1010111/pywaterflood/master/testing/data/"
prod = pd.read_csv(gh_url + 'production.csv', header=None).values
inj = pd.read_csv(gh_url + "injection.csv", header=None).values
time = pd.read_csv(gh_url + "time.csv", header=None).values[:,0]

crm = CRM(tau_selection='per-pair', constraints='up-to one')
crm.fit(prod, inj, time)
q_hat = crm.predict()
residuals = crm.residual()