Optimization of Financial Indicators in Shale-Gas Wells Combining Numerical Decline Curve Analysis and Economic Data Analysis

We present a methodology to determine optimal financial parameters in shale-gas production, combining numerical simulation of decline curves and stochastic modeling of the gas price. The mathematical model of gas production considers free gas in the pore and the gas adsorbed in kerogen. The dependen...

Full description

Bibliographic Details
Main Authors: Andres Soage, Ruben Juanes, Ignasi Colominas, Luis Cueto-Felgueroso
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Energies
Subjects:
Online Access:https://www.mdpi.com/1996-1073/17/4/864
_version_ 1797298340954636288
author Andres Soage
Ruben Juanes
Ignasi Colominas
Luis Cueto-Felgueroso
author_facet Andres Soage
Ruben Juanes
Ignasi Colominas
Luis Cueto-Felgueroso
author_sort Andres Soage
collection DOAJ
description We present a methodology to determine optimal financial parameters in shale-gas production, combining numerical simulation of decline curves and stochastic modeling of the gas price. The mathematical model of gas production considers free gas in the pore and the gas adsorbed in kerogen. The dependence of gas production on petrophysical parameters and stimulated permeability is quantified by solving the model equations in a 3D geometry representing a typical fractured shale well. We use Monte Carlo simulation to characterize the statistical properties of various common financial indicators of the investment in shale-gas. The analysis combines many realizations of the physical model, which explores the variability of porosity, induced permeability, and fracture geometry, with thousands of realizations of gas price trajectories. The evolution of gas prices is modeled using the bootstrapping statistical resampling technique to obtain a probability density function of the initial price, the drift, and the volatility of a geometric Brownian motion for the time evolution of gas price. We analyze the Net Present Value (NPV), Internal Rate of Return (IRR), and Discounted Payback Period (DPP) indicators. By computing the probability density function of each indicator, we characterize the statistical percentile of each value of the indicator. Alternatively, we can infer the value of the indicator for a given statistical percentile. By mapping these parametric combinations for different indicators, we can determine the parameters that maximize or minimize each of them. We show that, to achieve a profitable investment in shale-gas with high certainty, it is necessary to place the wells in extremely good locations in terms of geological parameters (porosity) and to have exceptional fracturing technology (geometry) and fracture permeability. These high demands in terms of petrophysical properties and hydrofracture engineering may explain the industry observation of “sweet spots”, that is, specific areas within shale-gas plays that tend to yield more profitable wells and where many operators concentrate their production. We shed light on the rational origin of this phenomenon: while shale formations are abundant, areas prone to having a multi-parameter combination that renders the well profitable are less common.
first_indexed 2024-03-07T22:33:33Z
format Article
id doaj.art-5f0e3a6c97d34b1a909bfbb0e0323891
institution Directory Open Access Journal
issn 1996-1073
language English
last_indexed 2024-03-07T22:33:33Z
publishDate 2024-02-01
publisher MDPI AG
record_format Article
series Energies
spelling doaj.art-5f0e3a6c97d34b1a909bfbb0e03238912024-02-23T15:15:17ZengMDPI AGEnergies1996-10732024-02-0117486410.3390/en17040864Optimization of Financial Indicators in Shale-Gas Wells Combining Numerical Decline Curve Analysis and Economic Data AnalysisAndres Soage0Ruben Juanes1Ignasi Colominas2Luis Cueto-Felgueroso3Group of Numerical Methods in Engineering—GMNI, Center for Technological Innovation in Construction and Civil Engineering—CITEEC, Civil Engineering School, Universidade da Coruña, Campus de Elviña, 15071 A Coruña, SpainDepartment of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USAGroup of Numerical Methods in Engineering—GMNI, Center for Technological Innovation in Construction and Civil Engineering—CITEEC, Civil Engineering School, Universidade da Coruña, Campus de Elviña, 15071 A Coruña, SpainDepartment of Civil Engineering: Hydraulics, Energy and Environment, Universidad Politécnica de Madrid, 28006 Madrid, SpainWe present a methodology to determine optimal financial parameters in shale-gas production, combining numerical simulation of decline curves and stochastic modeling of the gas price. The mathematical model of gas production considers free gas in the pore and the gas adsorbed in kerogen. The dependence of gas production on petrophysical parameters and stimulated permeability is quantified by solving the model equations in a 3D geometry representing a typical fractured shale well. We use Monte Carlo simulation to characterize the statistical properties of various common financial indicators of the investment in shale-gas. The analysis combines many realizations of the physical model, which explores the variability of porosity, induced permeability, and fracture geometry, with thousands of realizations of gas price trajectories. The evolution of gas prices is modeled using the bootstrapping statistical resampling technique to obtain a probability density function of the initial price, the drift, and the volatility of a geometric Brownian motion for the time evolution of gas price. We analyze the Net Present Value (NPV), Internal Rate of Return (IRR), and Discounted Payback Period (DPP) indicators. By computing the probability density function of each indicator, we characterize the statistical percentile of each value of the indicator. Alternatively, we can infer the value of the indicator for a given statistical percentile. By mapping these parametric combinations for different indicators, we can determine the parameters that maximize or minimize each of them. We show that, to achieve a profitable investment in shale-gas with high certainty, it is necessary to place the wells in extremely good locations in terms of geological parameters (porosity) and to have exceptional fracturing technology (geometry) and fracture permeability. These high demands in terms of petrophysical properties and hydrofracture engineering may explain the industry observation of “sweet spots”, that is, specific areas within shale-gas plays that tend to yield more profitable wells and where many operators concentrate their production. We shed light on the rational origin of this phenomenon: while shale formations are abundant, areas prone to having a multi-parameter combination that renders the well profitable are less common.https://www.mdpi.com/1996-1073/17/4/864unconventional resources of hydrocarbonseconomic geology of fossil fuelsnumerical decline curve analysiseconomic performance shale-gasshale-gas 3D production model
spellingShingle Andres Soage
Ruben Juanes
Ignasi Colominas
Luis Cueto-Felgueroso
Optimization of Financial Indicators in Shale-Gas Wells Combining Numerical Decline Curve Analysis and Economic Data Analysis
Energies
unconventional resources of hydrocarbons
economic geology of fossil fuels
numerical decline curve analysis
economic performance shale-gas
shale-gas 3D production model
title Optimization of Financial Indicators in Shale-Gas Wells Combining Numerical Decline Curve Analysis and Economic Data Analysis
title_full Optimization of Financial Indicators in Shale-Gas Wells Combining Numerical Decline Curve Analysis and Economic Data Analysis
title_fullStr Optimization of Financial Indicators in Shale-Gas Wells Combining Numerical Decline Curve Analysis and Economic Data Analysis
title_full_unstemmed Optimization of Financial Indicators in Shale-Gas Wells Combining Numerical Decline Curve Analysis and Economic Data Analysis
title_short Optimization of Financial Indicators in Shale-Gas Wells Combining Numerical Decline Curve Analysis and Economic Data Analysis
title_sort optimization of financial indicators in shale gas wells combining numerical decline curve analysis and economic data analysis
topic unconventional resources of hydrocarbons
economic geology of fossil fuels
numerical decline curve analysis
economic performance shale-gas
shale-gas 3D production model
url https://www.mdpi.com/1996-1073/17/4/864
work_keys_str_mv AT andressoage optimizationoffinancialindicatorsinshalegaswellscombiningnumericaldeclinecurveanalysisandeconomicdataanalysis
AT rubenjuanes optimizationoffinancialindicatorsinshalegaswellscombiningnumericaldeclinecurveanalysisandeconomicdataanalysis
AT ignasicolominas optimizationoffinancialindicatorsinshalegaswellscombiningnumericaldeclinecurveanalysisandeconomicdataanalysis
AT luiscuetofelgueroso optimizationoffinancialindicatorsinshalegaswellscombiningnumericaldeclinecurveanalysisandeconomicdataanalysis