We specialize in optimization and program management for R&D divisions of major Pharma and Biotech companies.
Risk Analysis should be a part of every decision a business makes. We are constantly faced with uncertainty, ambiguity, and variability. And even though we have unprecedented access to information, we can’t accurately predict the future. Monte Carlo simulation (also known as the Monte Carlo Method) lets you see all the possible outcomes of your decisions and assess the impact of risk, allowing for better decision making under uncertainty.
Monte Carlo simulation is a computerized mathematical technique that allows people to account for risk in quantitative analysis and decision making. The technique is used by our professionals in such widely disparate fields as finance, project management, energy, biotechnology, engineering, pharmaceuticals, insurance, oil & gas, transportation, and the environment.
Monte Carlo simulation furnishes the decision-maker with a range of possible outcomes and the probabilities they will occur for any choice of action.. It shows the extreme possibilities—the outcomes of going for broke and for the most conservative decision—along with all possible consequences for middle-of-the-road decisions.
The Petroleum Resource Management System categorizes petroleum resources into three major categories as shown in Figure 1.
- Prospective Resources are those petroleum resources that may be recovered from undiscovered accumulations in future projects;
- Contingent Resources, are quantities of petroleum anticipated to be commercially recoverable from known accumulations from projects that are not yet mature enough to be considered commercial; and
- Reserves are quantities of petroleum accumulations that are anticipated to be commercially recoverable from developed or soon to be developed projects.
Two Methods for Calculating Resources
Volumetric estimates of petroleum resources and reserves are subject to substantial uncertainty because they rely on subsurface data collected from widely spaced wells. For this reason the SPE Guidelines recommends that estimates be provided at three levels of certainty – Low, Medium (Best) and High (1C, 2C,3C or 1P, 2P,3P).
The SPE also allows an organization to make its estimates using either deterministic or stochastic estimating processes. Here we will show you how they may be calculated using the stochastic process and then, a little later, the deterministic process.
Both estimates rely on the volumetric equation for calculating stock tank oil originally in-place (STOOIP), in barrels, is as follows:
- STOOIP = 7758 (bbls/acre-ft) x Oil Reservoir Area (acres) x Average Net Pay Thickness (ft) x Porosity (fraction) x Oil Saturation (fraction) x Oil Shrinkage (1/Bo)
The Recoverable Oil Volume is equal to the STOOIP multiplied by the recovery factor, which depends on the reservoir properties and the recovery mechanism (e.g. natural drive, pressure maintenance, enhanced recovery methods). The Estimated Ultimate Recovery (EUR) is the volume of oil that may ultimately be recovered using the enhanced recovery mechanism that may be applied at some time in the future.
- Recoverable Oil (bbls) = STOOIP x Recovery Factor
- EUR (bbls) = STOOIP x Ultimate Recovery Factor
In the deterministic process the measured Low, Best and High values or estimates are used for each variable (e.g. area, average thickness, porosity etc) in order to calculate the three deterministic estimates for Prospective Resources.
Oil & Gas exploration involves many unknowns, with high risk and uncertainty an inherent part of the oil and gas industry. The elucidation and management of risk in oil and gas exploration has always been difficult.
Today, most petrochemical companies choose expert consultants to analyze risk and make more informed, lucrative decisions.
The use of Monte Carlo simulation tools in the petroleum industry is now common among the more sophisticated hydrocarbon exploration and production organizations. Nexus Energy has been using Crystal Ball for probabilistic assessment of its prospect inventory, resource and reserves volumes since the company started.
-Graham Bunn, Chief Petroleum Engineer, Nexus Energy Ltd.
Key deliverables to the oil and gas industry include sensitivity and tornado analysis, correlation, historical data fitting and optimization.
- The sensitivity analysis and tornado analysis are two separate methods that help you to understand which of the uncertain inputs (e.g., the recovery factor or the price of oil) drive the uncertainty in your models.
- Correlation lets you link uncertain inputs and account for their positive or negative dependencies.
- If historical data does exist, the data fitting feature will compare the data to the distribution algorithms and calculate the best possible fit and parameters for your data.
- Optimization helps determine optimal decision choices to maximize or minimize your goals (e.g., maximize the return on a portfolio of assets, optimal number of wells to drill), and the efficient frontier runs multiple optimizations to determine the best balance of risk and reward for a particular problem or portfolio.
Our team is specialized in Monte Carlo Simulations for the Oil and Gas Industry.