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Other literature type . 2026
License: CC BY
Data sources: Datacite
ZENODO
Other literature type . 2026
License: CC BY
Data sources: Datacite
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Application of Monte Carlo Simulation in Petroleum Engineering And Energy Management

Authors: Arindam Mukherjee;

Application of Monte Carlo Simulation in Petroleum Engineering And Energy Management

Abstract

Monte Carlo simulation is a powerful statistical technique for modelling uncertainty and predicting outcomes in a complex system of petroleum projects. This paper explores its application in energy management and petroleum engineering, highlighting its benefits and uses. Monte Carlo simulation is a probabilistic modelling technique used to quantify uncertainty in complex petroleum reservoir systems. Uncertainty is inherent in the petroleum and energy system due to variability in geological properties, market conditions and regulatory environments. Generally, the traditional deterministic approach often fails to capture the full range of possible outcomes. This study represents the application of Montecarlo simulation (MCS) as a robust probabilistic framework for uncertainty qualification in the Energy management and petroleum engineering projects. The methodology integrates statistical distributions of key uncertain variables—such as reservoir properties (porosity, water saturation, permeability), drilling parameters (pore pressure, in-situ stresses, equivalent circulating density), production decline parameters, commodity prices, and capital expenditures—into numerical models to generate thousands of simulation realisations. The results provide probabilistic outputs including P10, P50, and P90 estimates for reserves, production forecasts, mud weight windows, and net present value (NPV).The study demonstrates that Monte Carlo–based risk assessment significantly improves decision-making by quantifying the probability of adverse events such as wellbore instability, kick/loss scenarios, economic loss, and underperformance of energy projects. Furthermore, applications in renewable energy forecasting and carbon capture and storage (CCS) projects illustrate its broader role in sustainable energy planning and financial risk management. The findings confirm that probabilistic modelling enhances operational safety, economic reliability, and strategic planning compared to deterministic approaches. Monte Carlo Simulation is therefore established as a critical tool for modern risk-based petroleum operations and energy investment analysis.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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