Farm-out Strategy For Sharing Risk in Exploration Blocks

January 15, 2010 • Tell FriendsPrinter Friendly

This article will discuss how monte carlo simulation can be used to determine the optimal share to be farm out for sharing risk in an exploration prospect .


The high costs and risks associated with many oil and gas exploration projects often cause companies to seek partners to share those costs and risks before embarking on major expenditure programs.

In this case, partnerships terms in exploration program of block X are negotiated as farm-in deals in which Company (i.e. farmor) as the holder of this block license, agrees to assign part of its interest to a third party (i.e. farminee). To earn an interest in this exploration block, the farminee may agree to fund a specified part of the exploration work program. In other words, farminee will pay a certain percentage of the costs of an exploration well in to earn the certain percentage of working interest in block X.

A study of  farm out analysis for Block is conducted to optimize farm-out analysis using probabilistic model. The probabilistic methods have an advantage over deterministic methods by being able to systematically incorporate the risks associated with a range of future uncertainties in the exploration program.

A risked economic evaluation of a two pay zone prospect i.e. prospect A and B in block X illustrates how probabilistic simulation modeling and deterministic valuation and risk analysis techniques combine to provide useful insight to economic evaluation of a farm in opportunity.


In this case, Company had the perception of risk for prospect A and B in Block X i.e. 12% of chance of success.

Figure 1. Chance Tree for Field A and B

From figure1, it is clear that making a discovery in more than one prospect has a very low probability. Company calculates a probability of 3.24% of finding oil in two prospects.

It is instructive to analyze the dry-hole risked cost versus the total most likely EMV for the prospect.


Figure 2. Deterministic Risked Cost Exposures and Values for Block X

Figure 2 shows the deterministic calculations of these parameters for the most likely input assumption based on the previous study and the farminee for a range of farm-in terms (varying from the farminee paying 100% to 50% of the exploration well cost to earn a 50% working interest in the contact).

The risked dry hole cost versus the prospect EMV for Company with no farm-in terms (i.e. 100% prospect case) is plotted for comparison. As the farminee pays a greater share of the exploration well cost, the risked dry hole cost decreased for Company, reaching zero when the farminee pays 100% of the well cost and the EMV for Company increases.

From figure 2, it was suggested that the farminee would have to pay more than 50% of the exploration well costs to earn a 50% working interest for the EMV of Company to be greater than in the case where Company retains 100% working interest in block X.

Figure 2 also illustrates that all the farm-in terms analyzed reduce the dry hole cost exposure of Company relative to the no-farm-in case. Reducing cost exposure may be a more important driver to Company than maximizing EMV in negotiating farm-in terms.

Assumptions for a Probabilistic Simulation Model

Farm-in terms can be evaluated in further detail through risk analysis and evaluation that establish the probabilities associated with certain risked values and quantify the downside risk of both parties. This requires probabilistic methods based upon Monte Carlo Simulation. The objectives of probabilistic analysis would be to aid decisions specific for Company to decide whether it make sense to farm out or if it does make sense to farm out, on what promoted terms should it do so.

Using a probabilistic model to complement the deterministic cash flow model provides the analyst with more confidence in making such decisions than just using a deterministic model.


Table 1. Range Estimates for Input Variables

For the probabilistic Monte Carlo model, the study defines distributions for five parameters (table 1), which are to be used as input variables for the simulation. Oil price and reservoir productivity are the two parameters that have greatest impact on prospect value. The variables such as capital expenditure, operating expenditure, and production are varied through use of performance ratios.

In this study, all variables are modeled as triangular distributions, and all input parameters are assumed to be independent of each other. The most likely values for the triangular distributions are those used for the most likely deterministic calculation. One thousand iterations have been performed in each Monte Carlo simulation run.

Probabilistic Simulation Results


Figure 3. Cummulative Probability Distribution of Values for Block X

Figure 3 illustrates the EMV simulation results plotted as cumulative probability distributions for three different farm-in terms. The no-farm-in case distribution is plotted for comparison. The deterministic most likely EMV calculations for Company where farminee pays 50%, 80% and 100% of the exploration well cost are also shown.

The deterministic values lie to the high side of their respective probability distributions. The farm-in case distributions show significantly narrower ranges of values than the no farm-in case (i.e. less risk for Company in the farm-in cases regardless of farm-in terms within the range analyzed). The above diagram shows how the farm-in cases reduce upside potential as well as downside risked values of a prospect and focus on farm-in terms that fit a Company’s particular risk profile or preference.

Mean versus Most-likely EMV



Figure 4. Probabilistic and Deterministic Comparison for
Block X

Figure 4 plots the mean of the EMV @15% distribution for Company and the farminee versus different paying percentages of the exploration well costs relating to the range of farm-in terms analyzed for the farminee to earn a 50% working interest in the contract.

Figure 4 illustrates clearly how the mean EMV for Company increases as the mean EMV for the farminee decreases with the higher paying interest for the farminee. At 100% paying interest for the exploration well, the farminee has a negative mean EMV, indicating that such terms are unlikely to be acceptable to the farminee.

At 50% paying interest (i.e. “heads-up deal”) the mean EMV for the farminee is slightly the same as the mean EMV for Company, because it was assumed farminee places the same chance of success as the farmor on the prospect. The higher chance of success the farminee places on the prospect, the higher the EMV for the farminee.

Comparing the deterministic most likely EMV calculations with the means of the probabilistic EMV distributions as shown in the second chart in figure 4, both deterministic and probabilistic methods show similar trends, but the mean values are significantly higher than the most likely values. This results in the prospect overall looking more economically attractive from the mean value probabilistic calculations.

The no farm-in case (i.e. 100% prospect to Company) is plotted in figure 4 for both calculation methods. From Company’s perspective, its EMV for farm-in terms exceed the no-farm-in case at a 65% paying interest for the farminee based on the means of the probabilistic calculations, whereas based on the deterministic calculations this is only greater than a 50% paying interest. This is in spite of the absolute EMV’s for Company being higher for the mean of the probabilistic calculations.

It should be noted that the actual relationship between the most likely deterministic calculations and the mean of the probabilistic calculations for any given prospect will vary according to the input variables and assumptions applied.

Downside Risk from Probabilistic Calculations

One of the significant advantages of probabilistic methods is their ability to quantify downside risk in more detail than deterministic calculations. Analysis of the negative values in the calculated EMV distributions from the probabilistic method can yield significant insight into the downside risk associated with a prospect.


Figure 5. Indicators of Downside Risk Compared for Block X

Figure 5 shows two attributes of the negative portions of the EMV distributions for the X block. The mean of all the negative values in the EMV @ 15% distribution for Company and the farminee (termed mean downside risk) are plotted against the farminee’s different paying percentages of the exploration well cost (i.e., potential farm-in terms).

It is relative magnitude of the mean downside risk values that are meaningful in figure 5. For the no-farm-in case the mean downside risk of Company’s EMV is greatest (i.e. most negative). This is consistent with the greater range illustrated for the cumulative probability distribution of the no-farm-in case (figure3) reflecting its greater risk to Company. For the farm-in cases the risks are shared between the two parties in different proportions depending on the terms.

For the 50:50 (or the heads-up) case the risks are shared equally between Company and the farminee. As the farm-in term increases the promote paid, the farminee’s mean downside risk decreases. In all farm-in cases when the mean downside risk of the farminee and Company are combined, they approximately equal the mean downside risk for Company for the no-farm-in case.


During this farm-out program in Block X, there were two proposals from two Farminees (party A and B) who both have interest in this block.

Proposal A

Party A has proposed as follows :
– take 60% working interest and be operator after 3 years
– carry first well at the well cost with maximum exposure of $ 4.00 MM
– Excluding $2.60 MM signature bonus

Based on the result of probabilistic model, for 50% working interest, farminee should pay greater than 65% of exploration cost for increased the Company’s EMV rather than no-farm-in scenario. As our scenario in block X valuation, the total exploration cost from 2005 to 2007 is $11.5MM. If Party A only spend $4 MM or 34.5% of total exploration cost to take 60% working interest, this proposal is relatively not attractive for Company.

Proposal B

Party A and B agreed to get a joint venture with Company in this block with proportion as follow: 45% Company, 40% Party A and 15% Party B

The other conditions are as follows:
– Party A will commit to $4 MM drilling program regardless of the number of wells and 50% of
$2.3 MM signature bonus.
– Party B will commit to $2 MM drilling program regardless of the number of wells and 50% of
$2.3 MM signature bonus and after 3 years Party B has option to buy another 5% of Company share at
market price

This proposal combined exploration cost of $6 MM (52.2% of total exploration cost) from both farminees to get 55% working interest. Although the percentage of exploration cost share still lower than the probabilistic result i.e. 65% of exploration cost, this scenario is more attractive than the previous one because there is a signature bonus of $2.3 MM paid by both farminees.


Based on such an analysis of the block X Farm-out, there do appear to be farm-in terms that could be attractive to both parties i.e. Farminee pays between 60% and 70% of exploration well costs to earn a 50% working interest. However, If Company is subject to capital constraints and under time pressure to drill an obligation well it may accept farm-in terms with little or no promote to limit its financial exposure and down side risk. Alternatively, the farminee may be prepared to accept less than optimal farm-in terms on this prospect in exchange for an interest in the upside potential of other possible prospects within the permit.

This risked economic analysis, however comprehensive, is not by itself going to establish a deal or guarantee a profitable outcome. It will, nevertheless, provide both parties with significant insight into block X that should aid in better decision making for both parties.

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