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.

Getting started

The source can be downloaded from https://github.com/frank1010111/pywaterflood

Then, from the base directory, install the package with

pip install .

A simple example

import pandas as pd
from pywaterflood import CRM

prod = pd.read_csv('testing/data/production.csv').values
inj = pd.read_csv('testing/data/injection.csv').values
time = pd.read_csv('testing/data/time.csv').values[:,0]

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