Table 4: The Improved Efficient Frontier Prospect Portfolios’ Details

Table 5 shows the Mean Return (µ) and the investment fractions of total funds invested in each prospect. 

Table 5: The Investment Fractions in Improved Prospects Portfolios

 The improved Efficient Frontier of the prospect Minimal Mean-Variance portfolios has the following results.  The Efficient Frontier curve shows that an increase in the Mean Return of the portfolio causes an increase in the portfolio Standard Deviation. To emphasise, the Efficient Frontier gets flattered as expected. This shows that each additional unit of Standard Deviation (i.e., risk) allowed, increases the portfolio Mean Return by less and less. That is approximately the following points: 1. Main Return of 0.51 with Standard Deviation of 0.09336; 2. Main Return of 0.52 with Standard Deviation of 0.9455; 3. Main Return of 0.53 with Standard Deviation of 0.0958; 4. Main Return of 0.54 with Standard Deviation of 0.0972; 5. Main Return of 0.55 with Standard Deviation of 0.0986; 6. Main Return of 0.56 with Standard Deviation of 0.1001; 7. Main Return of 0.57 with Standard Deviation of 0.102; 8. Main Return of 0.58 with Standard Deviation of 0.1042;  9. Main Return of 0.59 with Standard Deviation of 0.107. 

Chapter 5: Production & Financial Forecast in Oil

In Book 6: Comprehensive Sensitivity Analysis of Risk for Businesses

ISBN: 978-620-2-91949-4

Author: Vojo Bubevski (Independent Researcher)

Abstract  

This chapter presents a stochastic model of production and financial forecast in the petroleum industry for the future ten years. The production, revenues, and present value are based on exponential decline calculated by using a specific formula. The uncertain inputs are calculated by specific probability distributions. The production and financial outputs are calculated for each year. The operating costs are fixed throughout the forecast. The first-year cost may be considered a capital investment. Also, the total Net Present Value (NPV) is calculated by using five possible values for Discount Rate in order to compare the effect of different discount rates on Total NPV.

Keywords: Risk Assessment and Management, Petroleum Industry, Production & Financial Forecast, Financial Risk, Sensitivity Analysis; Monte Carlo simulation; Stochastic model. 

The Results

The comparison of the five simulation results, considering Total NPV Mean, Standard Deviation, and Discount Rate, is presented in Table 1. 

Table 1: Comparison of Simulation Results ($ in Millions)

The Discount Rate starts at 0.08 (i.e., 8%) in Sim#1, and then linearly increases by 0.04 (i.e., 4%) in every next simulation, thus reaching 0.24 (i.e., 24%) in Sim#5 (Table 1). The Total NPV Mean starts at $155.782 Million in Sim#1, and then logarithmically is reduced to $56.850 Million in Sim#5. Similarly, the Standard Deviation starts at $60.201 Million in Sim#1, and then logarithmically is reduced to $21.22 Million in Sim#5. It is important to emphasise that as the Discount Rate linearly increases, the Total NPV Mean and Standard Deviation logarithmically decrease. 

Chapter 3: Oil Pipe Line Risk Analysis

In Book 8: Business Risk Analysis and Prediction

ISBN: 978-620-3-19675-7

Author: Vojo Bubevski (Independent Researcher)

Abstract

This chapter illustrates a stochastic model for risk analysis in an oil pipeline network. There are nine types of critical risks, and there are nine routes in the pipeline network. Each route has three characteristics: diameter, mean pressure, and distance. For each type of risk and each route, two variables are simulated: the number of events where that risk type occurs, and the typical magnitude of such a risk. These are then accumulated to find the severities of the risk types, by the route and total overall routes. The model assumes that sufficient historical data is available to estimate regression equations relating the frequency and mean magnitude of each risk type to the characteristics of the routes. The resulting coefficients are given. In addition to the usual constant and variable coefficients, also the error coefficients are given. The simulation calculates the outputs including summary statistics and a histogram for each risk type and for the total of all risk types.

Keywords: Risk Analysis, Oil & Gas, Severity Rout & Event Projection, Financial Risk, What-If Analysis; Sensitivity Analysis; Monte Carlo simulation; Stochastic model. 

The Results 

The calculated Total Severity per Rout is as follows. The calculated Total Severity per Rout starts at 1,070,387 in Rout 1 and increases to a maximum of 1,310,339 in Rout 2. Then it decreases to a minimum of 535,562 in Route 3. From Route 3 it alternatively increases and decreases to a local minimum of $585,206 in Route 8. Finally, it increases to 653,568 on Route 9. The Total Severity per Rout Mean (i.e., the Summary Trend) is the following. The Mean results, which involve a probability distribution calculation, are equal to the calculated results, which also use the probability distribution for calculation. Importantly, however, the Summary Trend presents the Mean, 25% – 75% Variability, 5% – 95% Variability, and Simulation Path.

Chapter2: Oil Drilling Decision Analysis

In Book 12: Operations Research Applications for Decision Analysis and Prediction

ISBN: 978-620-5-49772-2

Author: Vojo Bubevski (Independent Researcher)

Abstract 

This chapter demonstrates an example of Decision Analysis in oil drilling. A company must decide whether to drill an oil well at a particular site.  Drilling the well is expensive, and the company doesn’t know in advance exactly how much oil it will find, so the project is risky. The company has the option of running a geological test right away at a given cost, which will provide some indication of the amount of oil present.  The first decision is whether to run the geological test.  If the test is run, three results are possible, which indicate whether it is a dry, small, or large well. Each of these has a probability, given the amount of oil in the ground.  After each one of these test results, and also in the case where no test is run at all, the company must decide whether to drill with the given costs. The payoff from a dry well is zero; the payoff from a small and large well is given. The decision tree illustrates the structure of the decision problem. All monetary values and probabilities are given on the tree. The tree shows that the best strategy is to run the geological test and then drill only if the results of the test indicate a small or large well. Sensitivity analysis is performed to quantify the changes to the output variables as impacted by the changes to the input independent variables. The decision analysis results assist management to make decisions as necessary.

Keywords: Risk & Decision Analysis, Petroleum Industry, Oil Drilling, Profit, Financial Risk, Sensitivity Analysis; Decision Tree; Monte Carlo simulation; Stochastic model.

The Results 

The optimal decisions are identified by considering the Path Probability and Path Value of the end nodes of the tree. The decision tree tools resolve the optimal decision path and present it as a policy suggestion, i.e., the Optimal Decision Tree. The Optimal Decision Tree determined that the optimal strategy is to do a Test Drill. The Expected Value of the Entire Decision Tree Model is $545,000 M. The Optimal Decision Tree Model is described below. Branch 1: Test Indicates Dry Don’t Drill decision has Path Probability and Path Value of 38.00% and -$55,000 M respectively.  Branch 2: Test Indicates Small Drill decision has the following options: i) Dry Well with Path Probability and Path Value of 15.00% and -$655,000 M respectively. ii) Small Well with Path Probability and Path Value of 18.00% and $845,000 M respectively. iii) Large Well with Path Probability and Path Value of 6.00% and $2,745,000 M respectively.  Branch 3: Test Indicates Large Well Drill decision has the following options: i) Dry Well with Path Probability and Path Value of 5.00% and -$655,000 M respectively.  ii) Small Well with Path Probability and Path Value of 6.00% and $845,000 M respectively. iii) Large Well with Path Probability and Path Value of 12.00% and $2,745,000 M respectively.