P. Jeffrey Brown1 and Peter R. Rose2
Search and Discovery Article #40024 (2001)
1Rose and Associates, LLP., Austin TX ([email protected]);
2Rose and Associates, LLP., Austin TX ([email protected])
*Adapted for online presentation from poster session presented at AAPG Annual Meeting, Denver, CO, June 5, 2001.
Editorial Note: This article, which is highly graphic (or visual) in design, is presented as: (1) three posters, with (a) each represented by a small, low-resolution image map of the original from which each illustration or section of text on each poster is accessible for viewing at screen scale (higher resolution) by locating the cursor over the particular part of interest before clicking; and (b) each represented by a pdf image, which contains the usual enlargement capabilities; and (2) HTML text and figure captions linked to corresponding illustrations with descriptions.
Users without high-speed internet access to this article may experience significant delay in downloading several of these illustrations due to their sizes.
First Poster -Plays and Concessions
Second Poster -The Play Evaluation Process
Third Poster -Calculations and Output
It is an exploration “fact of life” that, while the Prospect is the economic unit of exploration, the Play is the operational unit. Due to the magnitude of expenditures (both money and manpower) and time framework involved, the most difficult and critical task in Exploration is selecting which plays in which to explore, not which prospects to drill.
We present a simple but powerful method for evaluating the geologic (and economic) chance, volume and value of geologic plays. The methodology is applicable for a spectrum of opportunities, from a medium- sized concession to a full geologic play. This monetization approach fills an ‘analytical gap’ between traditional methods for assessing volumes and geologic chance for plays (e. g., Baker et al., 1986,) and assessing the value of individual prospects (e. g., Rose, 1992; White, 1993).
Required inputs (only seven variables) are tied to company strategy (e. g., activity level, risk tolerance), and to units of natural measure (forecast geologic discoveries, their size distribution, and historic success rates) that can be validated against historical (or analog) results. The small number of requisite input variables encourages making multiple sensitivity cases for an exploration program.
Calculated outputs provide powerful information that can be used to prioritize a company’s exposure to various trends, leading to a portfolio of Plays. The process flow can be created quickly using Microsoft Excel and its embedded functions, as demonstrated in our poster session. Spreadsheets can be customized to model optimal activity levels and working interest, based upon a company’s risk tolerance.
Plays and Large Concessions can be Systematically And Objectively Evaluated for Undiscovered Potential -- Volumes, Value, and Chance of Success -- Just Like Prospects
Many terms are common to both analyses. Due to uncertainties associated with prediction, input variables should be entered as probabilistic ranges (e. g., P10/ P90).
Prospect Expected Value (Figure 1):
(Chance of Success X Success Case Value) LESS (Chance of Failure X Dry Hole cost)
Concession / Play Expected Value (Figure 2):
(Chance of Program Economic Success X (Success Case Value) LESS (Chance Program Failure) X (Dry Hole Program Cost [Failure Cost])
Expected Value (EV) is calculated by subtracting the chance-weighted capital exposure (funds at total risk) from the chance-weighted value, given success. Investing in a large number of projects with a positive Expected Value improves chance of profit.
For a prospect, generally one test is tolerated, with an associated ‘dry hole’ cost (cost to generate and drill the prospect). Success case value is estimated from a full-cycle cash flow analysis of field development, given a discovery.
For a family of prospects, a minimum program is modeled, reflecting the number of consecutive dry holes that would be tolerated before abandoning the play. The chance reflects the probability of making at least one economic discovery, and the ‘dry hole’ costs, are those associated with that minimum program.
Success case value is based upon the economic volumes found, given that the play proves economically viable and the modeled success-case exploration program is executed.
The play evaluation process requires the following:
1. Delineation of ‘Play’
2. Assessment of Geologic Chance
3. Assessment of Dry Hole Tolerance and Minimum Program Costs
4. Estimate of Success Case Activity Levels
5. Estimate of Economic Threshold Size and Value Per BOE/MCFE
6. Selection of Appropriate Field Size Distribution
The play should consist of prospects with similar geologic character and history.
Assessors should agree on the time period being analyzed (typically 5 years, 10 years, or total play life).
Decide if the assessment is for total industry, or just the prospects in which your company will participate.
To ‘value’ a play for a company (recommended), inputs should reflect company dry hole tolerance and success case activity levels.
For an individual prospect, detectable oil or gas is either present or not – much like an on / off switch.
Considering the family of prospects in Figure 4, as yet untested, there are elements of geologic chance that could condemn them all -- Shared Chance Factors.
Also, there are variables that can result in some being successful while others fail--Local Chance Factors.
The product of the Shared Chance Factors is called Play Chance—the chance that the play is viable. In proven plays this value is often set to certainty (1.0). Statistically, these factors are treated dependently.
The product of the Local Chance Factors is called Prospect Success Ratio -- the percentage of prospects that will be viable, if the overall play works. Statistically, these factors are treated independently.
This subdivision of chance is crucial to assessing the chance of program success, as explained below.
“How many totally dry holes would my company tolerate drilling prior to abandoning this play?”
This estimate is based upon:
· Company’s track record in similar plays
· Variability of prospects
· In some cases, minimum well commitment(s)
All costs associated with this minimum program must also be estimated (wells / seismic / land / manpower).
Estimate the level of exploration activity for the time period being analyzed, given that there will be at least one economic discovery made in the play.
To calculate overall play volumetric potential, enter a range for number of undrilled prospects, or predicted number of discoveries.
To model volumes captured by your company, enter a range for the number of prospects in which your company will participate, based upon:
· Company budget size
· Number of prospects in inventory, or number that is reasonable to assume could be acquired
· Time period being analyzed
“How large a field must we find to recover all full-cycle costs? What is a reasonable estimate for NPV per barrel / MCF for fields that will be found in this play?"
In addressing these questions, consideration should be given to:
· Time value of money
· Time period being evaluated
· Current infrastructure
Figure 6. Future field size distribution. It should reflect the size range from smallest detectable (‘geologic’) discovery up to the largest future discovery remaining to be found in the play. The threshold size is then used to estimate P(MEFS), the probability that any given discovery will exceed threshold (80% in this example). The size characteristics of just those fields exceeding the economic minimum (dashed yellow line) are used to calculate the economic volumes found, given success in the play.
Choosing the appropriate size distribution for FUTURE discoveries is crucial to realistic assessment of undiscovered volumes and value.
The distribution must reflect that the largest fields are often (but not always) found early in the ‘life’ of a play.
Fields tend to distribute themselves lognormally; that is, field sizes form a straight line on log - probability plots (Figure 5).
The future field size distribution should reflect the size range from smallest detectable (‘geologic’) discovery up to the largest future discovery remaining to be found in the play.
The threshold size is then used to estimate P(MEFS), the probability that any given discovery will exceed threshold (80% in the example in Figure 6). The size characteristics of just those fields exceeding the economic minimum (dashed yellow line in Figure 6) are used to calculate the economic volumes found, given success in the play.
Play / Concession Expected Value =
(Chance of Success X Value Generated) minus ((1-Chance of Success) X Failure cost)
This term represents the chance that at least one economic discovery will be made with the minimum program specified in Step 3 = Program Pe (Figure 7).
Since Local Chance Factors are treated independently, the chance of program success is simply one minus the product of all chances of total failure, as shown in the example in Figure 7.
Shared / play chance factors are shared by all prospects and are therefore kept as a constant in the calculation.
The number of dry holes tolerated has a pronounced effect on calculated chance of program success, particularly on the low end (e. g., 1 vs. 2, 2 vs. 3 dry holes tolerated).
This term, presented in Figure 8, represents value generated, given some success in the play, and that the success-case exploration program defined in Step 4 is executed.
Example Calculation in Figure 9 is based upon single- point input (we favor using probabilistic ranges). Dry hole exposure is the sum of all costs to execute the minimum program of 3 wells (company share). Play is assumed to be proven in this example (Play Chance = 1. 0).
Note that the success-case NPV is burdened with the costs of dry holes associated with the successful program.
Figure 10. Comparative measures—prospect scale vs. program / play scale. These are the key output variables from the analysis that can be incorporated into a business model for ranking a global portfolio of plays.
The factors listed in Figure 10 are the key output variables from the analysis that can be incorporated into a business model for ranking a global portfolio of plays.
This methodology has been coded into a user- friendly, Excel®- based software package that facilitates the analysis.
Baker, R.A., H. M. Gehman, W. R. James, D. A. White, 1986, Geologic Field Number and Size Assessment of Oil and Gas Plays, in Oil and Gas Assessment: Methods and Applications: AAPG Studies in Geology No. 21, p. 25 – 31.
Rose, P.R., 1992, Chance of Success and Its Use in Petroleum Exploration: Chapter 7: Part II. Nature of the Business in The Business of Petroleum Exploration: AAPG Treatise of Petroleum Geology, p. 71 – 86.
White, D.A., 1993, Geologic Risking Guide for Prospects and Plays: AAPG Bulletin, v. 77, p. 2048 – 2061.