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Journal Articles

Journal:
Journal of Petroleum Technology

Publisher: Society of Petroleum Engineers (SPE)

*Journal of Petroleum Technology*38 (03): 325–329.

Paper Number: SPE-13773-PA

Published: 01 March 1986

...J. Lohrenz; J.A. Pederson Summary Oil and gas properties should be evaluated by the distinguishing of searchable, developable, and producible

**reserve****values**. Petroleum engineers represent one discipline involved in such evaluations. The varying impacts of mainstream petroleum engineering...
Abstract

Summary Oil and gas properties should be evaluated by the distinguishing of searchable, developable, and producible reserve values. Petroleum engineers represent one discipline involved in such evaluations. The varying impacts of mainstream petroleum engineering contributions to these different evaluations are examined. Introduction Assigning costs, prices, and values for properties with oil and gas reserves requires the merging of expertise from multiple disciplines. Petroleum engineering is one of the disciplines involved. Geology, geophysics, and economics are others. Our purpose is to present a perspective for petroleum engineers in property evaluation. Properties have searchable, developable, or producible reserve values. Each kind of reserve has a different involvement with petroleum engineering practice. The term "searchable" is applied to reserves purposely. The traditional usage would be "exploration." To many, "exploration" implies a process that adheres to a priori expectations quite well. "Searching" implies a process of hunting for something that may not be found process of hunting for something that may not be found or may not exist. If something is found, it may not be what the hunter was after. A simple model of the sequence of searching for developing, and operating oil production is structured in the next section. Searchable, developable, and producible reserve values, as well as a cost of finding and a cost of finding and development, are defined mathematically. Values and costs are computed for three base cases that represent current properties. A key finding is that higher costs do not necessarily imply lower reserve values. The sensitivity of contributions from petroleum engineering and other disciplines is also studied. Estimates of future oil prices and their effect on reserve values are demonstrated. The relevant question treated is this: If the oil price is higher or lower, which costs change and when? Alternative answers to that question lead to a recommended answer for assessing reserve values. The overwhelming effect on searchable values of the probability of geological success and, given success, the probability of geological success and, given success, the amount of recoverable reserves found is shown. This leads to a reasonable explanation for what some might consider unreasonably large disagreements between bonus bidders for oil and gas leases. Searchable, Developable, and Producible Values Producible Values Consider the costs and values that arise in the sequence of searching for, developing, and producing oil. All costs and values are measured as present values appropriately discounted. The cost incurred in a search is (1) BXB is the tax-adjusted present value of bonus paid at time t=0; EXE is the exploration cost collected at t=tE, The cost of any development is (2) where development costs are aggregated at t=tD. The costs derived by Eqs. 1 and 2 can only be recouped by production having a positive net value starting at t=ti and terminating at t= ti + delta . With a constant rate of production, the producible value is production, the producible value is (3) Eqs. 2 and 3, including the value maximizing solution for delta, have been previously justified and treated in detail. Here, Eq. 1 incorporating the search costs has been appended. The model leads to searchable, developable, and producible values. Each search incurs the cost given by Eq. producible values. Each search incurs the cost given by Eq. With probability PS, geological success occurs. A recoverable reserve amount, Q >0, is found. With probability (1 - Ps), Q = 0, and the search, a geological probability (1 - Ps), Q = 0, and the search, a geological failure, ceases. If Q is sufficient that, with maximization of postexploration values, future values are positive, then and only then the geological success is developed and produced. In other words, if Eq. 3 is not greater than Eq. produced. In other words, if Eq. 3 is not greater than Eq. 2 with Q >0, the field found is not developed. There are really four distinct outcomes possible from any search: geological failure (Q = 0); geological success with an amount found insufficient to develop; geological success with an amount found sufficient to develop but not compensatory for search costs; and geological success with an amount found sufficient to develop and more than compensatory for search costs. The last three outcomes are all geological success; only the last outcome is an economic success. The probability of an economically successful search, PES, is always less than the probability of a geologically successful search, PS. PS. With a geological success, a positive amount, Q, is sampled from a log normal distribution of median size, RG, and variance, . JPT p. 325

Journal Articles

Journal:
Journal of Petroleum Technology

Publisher: Society of Petroleum Engineers (SPE)

*Journal of Petroleum Technology*36 (10): 1791–1796.

Paper Number: SPE-11299-PA

Published: 01 October 1984

... risk assessment calculation upstream oil & gas prospect conditional probability term continuum discovery investment artificial intelligence npv profit progression probability recoverable reserve geologic uncertainty

**reserve****value**take account risk analysis emv risk and uncertainty...
Abstract

Summary This paper proposes five risk analysis models for analyzing drilling prospects. The models range from a simple, two-outcome analysis (Level 1) to a full Monte Carlo simulation risk model (Level 5) that takes into account all geologic and economic uncertainties. The five levels offer an orderly plan for implementing risk analysis techniques in drilling prospect evaluations. Explorationists can enter the progression at any point, and then gradually expand and enlarge the scope of their evaluation model by following the stepwise progression. Introduction "How do I get started using risk analysis?" "We've been using a two-outcome risk evaluation model: dry hole or a discovery. We would now like to expand our risk assessment procedures. What is the next step for us?" "Is there a simple, first step for using Monte Carlo simulation? " Over the past 10 years petroleum exploration risk analysis has become a popular and topical subject in books and journals. As we extend our search for petroleum to the smaller, harder to find traps we have petroleum to the smaller, harder to find traps we have become increasingly aware of the risks of exploration. Uncertainties about future crude prices, inflation rates, supply and demand forecasts, etc. add to our concerns when evaluating exploratory and development well drilling prospects. Despite this increased awareness of risk and uncertainty, the actual use of quantitative risk analysis techniques vanes considerably. Some use sophisticated Monte Carlo simulation techniques. For many, though, quantification of risk consists only of making a statement about the chance of discovery, such as "we have a one-in-four chance of making a well."In this paper, I outline a systematic progression of five different levels, or models, for analyzing drilling prospects. Level 1 is a basic, two-outcome model prospects. Level 1 is a basic, two-outcome model representing one end of the risk model spectrum. Succeeding levels are increasingly comprehensive, extending finally to a full Monte Carlo simulation analysis in Level 5. This final model represents a state-of-the-art risk evaluation that can account for all uncertainties relating to geologic and economic factors. Five levels are proposed because they offer an orderly plan for implementing risk analysis. You can enter the plan for implementing risk analysis. You can enter the progression at the point of your present analysis method, progression at the point of your present analysis method, and then gradually expand and enlarge the scope of the analysis by following the stepwise progression. If you are just getting started you can begin at Level 1. The five models can be used for evaluating any management strategy for a given prospect such as drilling, farm out, electing to take a back-in option or dry hole contribution, etc. The models are also completely general in the sense they can be used for any type of drilling prospect whether gas, oil, offshore, onshore, exploratory well, or development well. Note, however, that because of the nature and magnitude of the uncertainties involved probably only Levels 4 or 5 will be adequate for offshore and frontier exploration prospects. General Model Characteristics All five risk models outlined here have several common features and assumptions. First, the models are based on the premise that the desired decision-making parameter is an expected value profit measure. "Expected valued" here is used in the sense of the fundamental concept of mathematical expectation-the cornerstone of all formalized strategies for decision making under conditions of uncertainty. An expected value profit is a weighted-average profit, where the weighting factors are the probabilities of occurrence of each possible outcome. Calculation consists of multiplying the profit (or loss) associated with each possible outcome by its respective probability of possible outcome by its respective probability of occurrence. These product terms then are summed algebraically to give the expected value profit (or loss) of the decision strategy. This decision-making parameter is usually given as the expected monetary value profit (EMV) of the option. The decision rule is to accept the decision alternative if its EMV is positive and to reject the alternative if its EMV is negative. For ranking purposes, alternatives that maximize positive EMV are selected. Expected value profit is the only criterion available that allows us to profit is the only criterion available that allows us to incorporate quantitative statements of risk (probabilities) into the evaluation process. For this reason the five risk models are structured to yield an EMV as the final decision-making parameter. For a more in-depth review of the expected value concept, see Chap. 3 of Ref. 1. There are two general types of models proposed. The first three models in the progression are discrete-outcome models, which take account of two or more discrete outcomes occurring. JPT P. 1791

Journal Articles

Journal:
Journal of Petroleum Technology

Publisher: Society of Petroleum Engineers (SPE)

*Journal of Petroleum Technology*33 (02): 341–348.

Paper Number: SPE-9357-PA

Published: 01 February 1981

...T.A. Petrie; Robert D. Paasch Summary U.S. oil

**reserve****values**have been affected significantly by recent changes in government pricing regulations and taxation. This paper presents a discounted cash flow model for the valuation of a hypothetical, though representative, producing property. The...
Abstract

Summary U.S. oil reserve values have been affected significantly by recent changes in government pricing regulations and taxation. This paper presents a discounted cash flow model for the valuation of a hypothetical, though representative, producing property. The analysis addresses changes in present values for various major categories of oil (i.e. lower-tier, upper-tier, stripper, newly discovered, and qualified tertiary recovery oil). In addition, we examine the differences in a property's value attributable to varying windfall profit tax treatment of major oil companies vs. that of independent producers. Introduction Recent modifications of U.S. energy pricing policies have important implications for the value of both existing and undiscovered conventional oil reserves. The key changes examined in this paper include the following. In April 1979, the Carter administration decided to phase out gradually price controls on domestic crude oil classified as lower and upper tier. Under this program, oil in the lower-tier category is being released to upper-tier status at the rate of 3% per month. In addition, upper-tier oil is being released to the free market price at the rate of 4.6% per month . As a result of these measures, all domestic oil will be selling at free market prices as of Oct. 1, 1981. These changes are summarized in Figs. 1 and 2 and Tables 1 and 2. Under the Crude Oil Windfall Profit Tax of 1980, the price benefits of this decontrol program over a prescribed base price are subject to an excise tax (after partial adjustment for severance taxes). In the case of independent producers, the excise tax rate is 50% on the first 1,000 bbl of daily output and 70% thereafter. For major oil companies, the rate on lower- and upper-tier oil (Tier 1) is 70% on the increment over a base price of $12.81/bbl adjusted for inflation subsequent to June 1979. In reality, the economic effect of the windfall tax on lower- and upper-tier oil is not unlike the continuation of price controls at a significantly higher price and with improved escalation provisions. In its final version of the windfall profit tax, Congress also decided to apply the excise tax to stripper oil (Tier 2), with independent producers subject to a reduced 30% levy (again up to a total 1,000-B/D limit) and majors and other producers subject to a 60% tax on the realized price increment above a $15.20/bbl base adjusted for inflation after June 1979. In the case of stripper oil, which already was decontrolled, the imposition of the windfall tax has had the effect of a price rollback on the producer. Effective June 1, 1979, the Carter administration began to allow newly discovered oil to receive an uncontrolled market price instead of an upper-tier price. At the same time, newly discovered oil also was redefined to be oil from an onshore property from which there was no production in 1978 or from an offshore property leased after Dec. 31, 1978, from which there was no 1978 production. The excise tax rate on this oil is 30% for all producers, and the base price was set at $16.55/bbl with an adjustment provision for inflation plus an additional 2% incentive escalation subsequent to June 1979. Escalation adjustments to base prices reflect the GNP deflator as a measure of inflation and are posted with a two-quarter lag.

Journal Articles

Journal:
Journal of Petroleum Technology

Publisher: Society of Petroleum Engineers (SPE)

*Journal of Petroleum Technology*21 (12): 1521–1527.

Paper Number: SPE-2439-PA

Published: 01 December 1969

... whose internal structure (gas

**reserves****value**, aquifer geometry and spatial distribution of aquifer characteristics, h, k) can be ascertained through direct measurements only within large limits of error. This is especially true for the aquifer, for which the sources of information are usually limited to...
Abstract

Water-Drive Gas Reservoirs: Influence of Pulse-Testing on the Indetermination Pulse-Testing on the Indetermination Range of Reserve Estimates For water-drive gas reservoirs, the accuracy of reserve estimates that are based on past history depends upon the practical possibility of applying to the reservoir, for long periods of time, pulsed rates of large amplitude. Introduction Estimating the reserves of a water-drive gas reservoir from its past production history leads to results that are intrinsically affected by some indetermination. The indetermination range, which can be greatly widened by errors in past performance data, becomes especially important when the reservoir has been produced at a fairly constant rate. produced at a fairly constant rate. Following a suggestion made by Muskat, this paper investigates the possibility of narrowing the paper investigates the possibility of narrowing the reserves indetermination range by depleting a gas reservoir, at the very beginning of its life, with a "squarewave" production rate program. The effects of frequency, shape, amplitude and number of cycles of the square-wave production program upon the range of indetermination have been program upon the range of indetermination have been investigated with reference to a hypothetical gas reservoir. Practical limitations were given due consideration. Theoretical Considerations A gas reservoir-aquifer ensemble is a nonlinear system whose canonical equations are known and whose internal structure (gas reserves value, aquifer geometry and spatial distribution of aquifer characteristics, h, k) can be ascertained through direct measurements only within large limits of error. This is especially true for the aquifer, for which the sources of information are usually limited to some peripheral wells that produce water. The reservoir-aquifer system is therefore a "black box". It is known that for a black box the principle of indetermination holds: accordingly, the number of different internal structures that can account for the same external behavior (i.e., the same response to an external stimulus) is infinite. As a consequence, it is impossible to ascertain the internal structure of a reservoir-aquifer system (its initial gas reserves value in particular) from the reservoir past performance. The fact that a unique solution of the problem cannot be found is only of theoretical interest. From a practical point of view, it is important to evaluate the practical point of view, it is important to evaluate the range of indetermination encompassing the initial gas reserves value as determined from the reservoir past performance. It is known that this range can be very performance. It is known that this range can be very large when the past performance data are affected by experimental errors, even of modest size. It must be pointed out that the reservoir-aquifer systems are subjected to a special constraint. Contrary to other physical systems that the information theory deals with, the reservoir-aquifer systems cannot be subjected more than once to a predetermined type of external stimulus, as it is impracticable to restore them to initial conditions; for example, their frequency-response characteristics cannot be determined. A theoretical analysis of the problem shows that a square-wave stimulus is the most suitable one to determine the transfer function (that is, the external behavior) of a system subjected to the kind of constraint noted in the preceding paragraph. JPT P. 1521

Journal Articles

Journal:
Journal of Petroleum Technology

Publisher: Society of Petroleum Engineers (SPE)

*Journal of Petroleum Technology*19 (02): 237–244.

Paper Number: SPE-1480-PA

Published: 01 February 1967

... numerical or analogical methods. Results obtained for six gas fields are reported. All these fields were produced with small fluctuations in their production rates, as is common practice for gas reservoirs; no gas storage fields were considered. Results obtained show that

**reserves****values**in a range of 1 to...
Abstract

The use of the material balance equation to estimate the volume of hydrocarbons originally present in a reservoir, whose producing mechanism is partly due to water drive, has been discussed in the literature by several authors. There is no general agreement upon the possibility of obtaining reliable results by this method. Gas reservoirs in contact with an active aquifer are considered in this paper. Theoretical considerations based on the cybernetic principle of uncertainty (which states that the internal structure of a system cannot be uniquely determined from its observed external behavior) lead to the conclusion that the volume of gas originally present in a reservoir of this type cannot be uniquely determined from its past history. The range of values which encompasses the actual value of the reserves varies from case to case and must be determined by either numerical or analogical methods. Results obtained for six gas fields are reported. All these fields were produced with small fluctuations in their production rates, as is common practice for gas reservoirs; no gas storage fields were considered. Results obtained show that reserves values in a range of 1 to 2, associated with appropriate aquifers, allow the matching of the reservoir past history with mean-square deviations less than the experimental errors involved in pressure and production measurements. Similar results have been found in several other partial water drive gas reservoirs. From these results it is concluded that gas reserves cannot be uniquely determined from the past performance of partial water drive reservoirs, at least in cases where the reservoir has been submitted to small fluctuations in the production rates, and pressure data of normal accuracy are available. Introduction A number of authors have analyzed the problem of estimating the reserves originally present in a partial water drive reservoir from its past pressure-production performance. Literature which deals with this subject can be grouped, according to their conclusions, as follows. A reliable value for the reserves can be obtained even if reservoir data (pressure and cumulative production) are affected by errors within the normal range. A reliable value for the reserves can be obtained only if reservoir data are very accurate or if past production performance has been subjected to abrupt variations in the production rate. No unique value for the reserves can be obtained from reservoir past production performance. This conclusion has been based upon theoretical considerations and verified in several field cases. The purpose of this paper, which deals only with partial water drive gas reservoirs, is to test the above conclusions against actual field cases. Some theoretical considerations on this problem are also presented. THEORETICAL CONSIDERATIONS As the behavior of a gas reservoir communicating with an aquifer depends on both the aquifer and the reservoir characteristics, the physical system to be studied is the combined gas reservoir plus aquifer. The information which is available for studying the performance of such a system is the well production rates and bottom-hole pressures, all given as functions of time. The external behavior of the reservoir-aquifer system is therefore described by 2n input variables (Gpj, Wpj) and n output variables (pj), n being the number of wells in the reservoir. In reservoir engineering it is common practice to consider the reservoir as a whole, disregarding the internal distribution of pressures and of producing wells. This practice is equivalent to substituting the above multivariable system with a single-variable system, where the average reservoir pressure is the only output variable and the cumulative production Gp(t) and Wp(t) are the input variables. The internal structure of such a system, defined by initial gas reserves G, aquifer shape and dimensions, boundary conditions and petrophysical parameters distribution throughout the aquifer, is unknown. Therefore, from a cybernetic point of view the system is a "blackbox". It has been demonstrated that for a black box the indetermination principle holds. Accordingly, the number of different internal structures (or set of parameters) which can account for the observed external behavior is infinite. As a consequence, the initial reserves cannot be uniquely determined from the reservoir past performance. JPT P. 237ˆ