The main objective of this project is to prepare the repor and make a presentation on the analysis for decision making, under uncertainty problem. Because, the client contracts the bank seeking advice on whether they shoul drill a well to look for oil or not. If client immediately proceeds with drilling, it will take one year to determine, if the well is success or failure. If client does not proceed with drilling, they will lose money. If drilling is successful, the clients will immediately start extracting the oil at a constant rate for the next five years. So, the senior vice president has asked the user to prepare a report and make a presentation on analysis and recommendations for decision making under uncertainty problem. In decision making process, under uncertainty problem, there are various potential uncertain elements such as,
These uncertainty problems will be estimated and effective outcomes for the tricky problem will be provided.
To resolve the uncertainty problem, estimate the following aspects by using the provided data such as (BINDER, 2019),
The estimation of success and failure is used to provide whether the drill is wet or dry. It is the main decision making element of this analysis. The estimation of success and failure is calculated by the U.S. Dry Exploratory and Developmental Wells Drilled Number of Elements and the U.S. Crude Oil Natural Gas and Dry Exploratory and Developmental Wells Drilled Number of Elements data and it pretends 2010 as 2018.
The estimation cost of drilling is used to provide range of coast of drilling growth rate and it is calculated by using the U.S. Nominal Cost per Crude Oil Well Drilled (Thousand Dollars per Well) and U.S. Nominal Cost per Crude Oil dry Drilled (Thousand Dollars per Well) data. It pretends 2010 as 2018.
The estimation of production rates is used to provide annual oil production for the US.
The estimation of operating expenses is used to provide the overall operating expenses which is used to resolve the problem of uncertainty decision making.
The estimation of future oil prices is used to provide the future crude oil prices by using FRED. FRED is used to provide the economic data for the required organization. In this estimation, the Crude oil Prices for West Texas Intermediate Company is used and it pretends 1986 as 2017. It is also used to resolve the uncertainty problem. It will be resolved by calculating the growth rate and its mean and standard deviation.
Finally, estimate the backward and forward induction using the Monte Carlo simulation. When faced with significant uncertainty in the process of making an estimation or forecast, the Monte Carlo simulation might prove to be a better solution. It is used to model the probability of various outcomes in a process that cannot easily be predicted due to the intervention of random variables.
The backward and forward inductions are real options for Monte Carlo simulation. These two options are used to provide better solution for drilling problems. Based on Monte Carlo simulation, the researcher has estimated whether drilling well is success or not.
Generally the backward and forward induction simulations are doing the all types of estimation and forecast. So our analysis, the range of success, oil price, production rate, operating expenses is related to the forward and backward induction. The forward and backward inductions are used to provide the better solution for uncertainty problem. These two options are effectively provide the decision based on uncertainty problem because, the backward induction is check whether the drilling is successful or not. And, the forward induction is used to check whether the drilling should happen or not. So, it will helpful user to make the drill or not drill decision.
In this section, the uncertainty problem is analyzed. Then, this problem is resolved by using the backward and forward induction simulation. Basically, all types of datasets support the future forward and backward induction. Thus, our dataset also supports the forward and backward analysis.
The estimation of range of success and failure is calculated by using the recent 10 years data, because it is recent and therefore more relevant. The drilling processes are highly presented compared to the other years and more clients are interested to extract the oil on recent 10 years. Thus, the researcher uses the recent 10 years data to estimate the success and failure rate of drilling.
Estimating the range for the probability of failure and success for data is used to calculate the success rate and failure rate which is obtained by # of the U.S. Crude Oil, Natural Gas and Dry Exploratory and Developmental Wells Drilled and # of the U.S. Dry Exploratory and Developmental Wells Drilled (Number of Elements) data. The estimate a range for the probability of success and failure pretends 2010 as 2018. The estimation of the success rate and the failure rate is illustrated below (Chang, 2011).
|
Success Rate |
|
|
Min |
0.86 |
|
Max |
0.90 |
|
Failure Rate |
|
|
Min |
0.14 |
|
Max |
0.10 |
Estimation of drilling success rate and failure rate is displayed in Appendix.
The estimation cost of wet and dry drilling well is calculated by using the U.S. Nominal Cost per Crude Oil Well Drilled value and U.S. Nominal Cost per dry Well Drilled value. To estimate the cost of the wet and dry drilling, calculate the growth rate for the crude oil well’s drilled values. The growth rate is calculated by using the Wet drilled values per year. The calculated growth rate is illustrated below.
|
|
The estimation of growth rate has substantial impact on the estimated values. It is used to obtain the insightful results. The growth rate for wet and dry well is one of the key drivers of backward and forward induction and its estimation should rely on a procedure that is objective as possible. Basically, the Monte Carlo simulation assumes that the returns are volatile. This causes the annualized growth rate to be lower than the expected annual return. Based on the obtained growth rate for the wet and dry well, the wet well has low growth rate when compared to the dry well. Thus, it has high cost of drilling in recent 10 years. Estimation cost of drilling the wet and dry well is displayed in Appendix (Dasgupta, 2010).
The estimation of production rate is calculated by using the US oil production data. It contains the oil wells, percentage of oil wells, annual oil production and oil rate per well. Based on oil per well, the production per well is calculated. This yields on 2017. Here, because of the production rates, only the last year data is applied because the rates were similar for each of the past ten years, and it was simplest to apply the last alone. To calculate the production per well, use the below mentioned formula,
Production per well = Oil per well value * 365(Year Days)
It is illustrated below.
|
Annual oil production |
|
|
Min |
117.89 |
|
Max |
154742.48 |
Based on production rate estimation, the daily rate*365 is used, because the research intends to estimate the annual oil production rate based on the daily rate. Thus, the daily rate * 365 formula is used to estimate the production per year values. Based on the production rate per values, the minimum and maximum production rates for annual oil production are estimated. Also, the 2010 production rate is calculated, and it is illustrated below.
|
Annual oil production |
|
|
Min |
117.53 |
|
Max |
6197687.225 |
Based on the Production 2010 and 2017, the production rates of 2010 has high annual production rate when compared to the production rates of 2017. Estimation of Production rates is displayed in the Appendix.
The assumptions of operating expenses are listed below:
Because, the lease operating expenses are assuming that water disposal costs are included in lease operating expenses and its operating costs are extremely variable and it make up a majority of costs. And, the transportation, gathering and processing expenses are considering the costs of condensate and oil, because we are not drilling for dry gas. The oil play uses the gathering lines for gathering the not trucks because it gathering the lines are significantly less expensive than trucks. The transportations uses the pipelines which is used to transport the oil, we have gathered at oil play to refine the sites, because it provides safety and it generally contains less expensive transportation method compared to rail transport. Estimation of operating expenses is displayed in the Appendix (McLeish, 2013).
The estimation of future oil prices is used to provide the future crude oil prices by using FRED. It is used to provide the economic data for the required organization. In this estimation, the Crude oil Prices is used for the West Texas Intermediate Company yield year is 1986 to 2017. To resolve the uncertainty problem, calculate the growth rate by using the crude oil prices’ values and also determine the mean and standard deviation for the growth rate. It is illustrated below.
|
Growth Rate |
|
|
Mean |
0.068409 |
|
Standard Deviation |
0.238406 |
To estimate the price oil, by calculate the growth rate because, it obtains the insightful results of our analysis. The Monte Carlo simulation requires the growth rate to estimate the drilling processes. It is one of key drivers the forward and backward induction. The growth rate is used to provide the substantial impact on the estimated values. In Monte Carlo simulation, it is required to calculate the overall oil extraction by estimating the standard deviation and mean for estimated growth rate. Thus, here the oil prices growth rate, mean and standard deviation are calculated.
Finally, after doing all types of forecasting, the researcher has estimated the backward and forward induction by using the Monte Carlo simulation, when facing significant uncertainty in the process of making a forecasting. It is used to model the probability of various outcomes in a process that cannot easily be predicted due to the intervention of random variables. Generally, the backward and forward inductions are real options for Monte Carlo simulation. These two options are used to suggest whether drilling is a good decision or not. To start with, it is required to look at the type of client in the case. Risk neutral agents are neither love nor hate risk; they focus on how much they can get on average rather than having a preference over risk. Therefore, they’ll be indifferent between facing an uncertain situation and the expected value of that uncertain situation., and clearly comparing expected value of drilling or not drilling will be a much easier and effective way in this case.
The processes of backward and forward induction for our analysis are based on the range of success and failure drilling, operating expenses, Annual oil extraction, and Production rate growth. To provide effective solution for our analysis, the backward and forward induction in Monte Carlo simulation requires (McTaggart, Findlay & Parkin, 2013):
These are already estimated as above. In backward induction and forward induction, the researcher combines all the estimated results and provides the effective solution for the analysis.
Based on the backward induction, the analysis provides the PV drill value as 12, 50,209.43 based on Option 1 Drill table. The Option 1 drill table contains all the estimated results such as PV for oil profits, cost of drilling dry well, cost of drilling dry well, drilling successful rate and drilling failure rate. Based on the overall estimated cost, the EV drill value is calculated to be 12, 50,209.43. The optimal decision is 0, 1. If optimal decision provides the result as 0, then it indicates do nothing, if it is providing the result as 1 then it indicates the drill. In our results, it provides 1, which is used to extract the oil. Hence, by drilling successfully, the clients will immediately start extracting the oil at a constant rate for the next five years. A decision tree for backward induction is illustrated below.
Based on forward induction, it provides the PV drill value is 1,17,14,873.04 based on Option 1 Drill table. The Option 1 drill table contains all the estimated results such as PV for oil profits, cost of drilling dry well, and cost of drilling dry well. Based on Option 1 drill table, it provides the drilling process to be successful because, basically the forward induction is used to indicate whether the process happen or not. And, based on the optimal decision table, the PV drill value is calculated to be 1,17,14,873.04. The optimal decision is 0, 1. If optimal decision is provide the result is 0, then it indicates do nothing, if it is providing the results as 1, then it indicates the drill. In our results, it shows 1, which is used to extract the oil. Thus, by drilling successfully, the clients will immediately start extracting the oil at a constant rate for the next five years. A decision tree for the forward induction is illustrated below.
These two options are effectively provide the decision based on uncertainty problem. The difference between the backward and forward induction is, the backward induction is check whether the drilling is successful or not. And, the forward induction is used to check whether the drilling should happen or not. So, it will helpful user to make the drill or not drill decision.
In our analysis, the Monte Carlo simulation is selected because when significant uncertainty is faced in the process of making an estimation or forecast, the Monte Carlo simulation might prove to be a better solution. In Monte Carlo simulation, it provides the McSim optional decision which is used to provide optimal solution for our analysis. In our analysis, the McSim optimal solution provides that the Drill is successful.
Recommendations and Conclusion
This project has successfully prepared a report and makes the analysis for the decision making under uncertainty problem. The uncertainty problems are resolved by using FRED and Monte Carlo simulation. These two methods are used to provide effective solution for an uncertainty problem. As Monte Carlo Simulation provides the optimal solution. So, the user recommends that the client must drill the well to look for oil, because based on the Monte Carlo simulation the drilling is successful. Hence, the client can immediately start extracting the oil at a constant rate for the next five years. If user decided to drill, it will provide the effective outcomes expected to more because if user did not drill, it provides the loss of money. So, it is good decision for uncertainty problem.
References
BINDER, K. (2019). MONTE CARLO SIMULATION IN STATISTICAL PHYSICS. [Place of publication not identified]: SPRINGER.
Chang, M. (2011). Monte Carlo simulation for the pharmaceutical industry. Boca Raton, FL: CRC Press.
Dasgupta, P. (2010). Economics. New York, NY: Sterling.
McLeish, D. (2013). Monte carlo simulation and finance. Hoboken, N.J.: Wiley.
McTaggart, D., Findlay, C., & Parkin, M. (2013). Economics. Frenchs Forest, N.S.W.: Pearson.
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