This paper describes a comprehensive methodology to rank drilling locations in a very large, unconventional tight-oil area based on both surface and subsurface characteristics. A North American case study is presented. Operators entering new prospect areas with little well control must make decisions on locating limited numbers of exploratory wells. Large prospect areas can be characterized by a variety of different mineral rights, surface-permitting obligations, uncertainties in geologic subsurface characteristics, complexity of topography, a variety of available infrastructures, potential operational issues, and environmental and regulatory challenges. The decision to appraise an acreage position is very complex and has a high level of risk. One solution to rank and risk candidate drilling locations is a procedure that considers the most critical variables needed for appraisal. The variables are used to generate optimized drilling location scenarios under various uncertainties and constraints using a mathematical probabilistic-optimization procedure and a ‘scorecard’ of surface cultural characteristics.

Because today's oil and gas industry recognizes the value of coalbed methane (CBM) resources, CBM exploration and development activities, once uniquely North American, are now under way on a global scale. Many publications address technically recoverable resources (TRR) of CBM in small geographic areas. However, there is little publicly information about the magnitude of TRR for coalbed methane on a global scale. Uncertainty exists in geologic and engineering data and, consequently, in the results of calculations made with these data. The uncertainty of the problem begs for a probabilistic solution. Our objective was to determine distribution of CBM TRR worldwide. To achieve this goal, we developed a computer program, called Unconventional Gas Resource Assessment System (UGRAS). The program integrates a Monte Carlo technique with an analytical reservoir simulator. The combination provides an excellent means to predict production profiles for a wide variety of reservoir characteristics and producing conditions. Using UGRAS, we established the probabilistic distribution of original gas-in-place (OGIP), TRR and recovery factor (RF) for selected coals in U.S. and derived a representative recovery factor distribution for CBM reservoirs. CBM recovery factors follow a log-logistic distribution, with a P50 of 38%. We extended the distribution of gas recovery factors gained from our analyses of U.S. coals to estimate global CBM TRR. Global technically recoverable CBM is estimated to be 500 (P90) to 3,000 (P10) Tcf, with a P50 value of 1,600 Tcf. UGRAS is a robust tool that may be used to evaluate and rank coalbed methane gas resources worldwide. Results of this work verify the existence of significant technically recoverable coalbed methane resources worldwide and can guide industry to better target its exploitation efforts in coals, worldwide.

The Eagle Ford shale is the shale with the greatest expectations in Mexico, given its abundant hydrocarbon liquids production and success in the US. Resource estimates were first published by the EIA and PEMEX in 2011 , and new production and geologic information has been obtained in the area since then. The objective of this paper is to provide an important updated assessment of oil and gas reserves, as well as contingent and prospective resources, in the Mexican Eagle Ford shale and to quantify the related uncertainties. Given the considerable uncertainties in shale reservoirs, a probabilistic decline-curve approach was chosen, as it allows performing a play-wide assessment with available production data, and it quantifies the uncertainty in the resource size. Analog areas in the US Eagle Ford shale were defined based on available geologic information in both the US and Mexico. The Duong model coupled with a Markov Chain Monte Carlo (MCMC) methodology was used to analyze and forecast production of wells located in the defined analog sectors in the US Eagle Ford shale. By combining the results of individual-well analyses, a type curve and estimated ultimate recovery (EUR) distribution was obtained for each of the defined analog sectors. These distributions were combined with well-spacing assumptions and sector areas to generate the prospective-resources estimates. Similar methodology was used to estimate the reserves and contingent resources associated with the few existing wells. As of March 2013, the total prospective resources (P90-P50-P10) for the Eagle Ford shale in Mexico are estimated to be 527-1,139-7,268 MMSTB of oil and 17-37-217 TSCF of gas. Estimated reserves associated with existing wells and corresponding offset well locations are 18-35-60 BSCF for gas and zero for oil. Estimated contingent resources are 14-64-228 MSTB of oil and 8.5-13-26 BSCF of gas. For comparison, total resources (including reserves) for the US side are estimated to be 2,950-8,250-23,530 MMSTB and 34-91-266 TSCF, as reported by Gong et al. (2013) . The results of the work presented here should provide a more reliable assessment of the resource size and uncertainty in the Mexican Eagle Ford shale for use in resource exploitation planning.

A practical probabilistic method is presented to administer the evaluation of proved undeveloped (PUD) oil and gas reserves in resource plays. In 2009, the Security and Exchange Commission (SEC) adopted revisions to the oil and gas reporting rules which allowed for the opportunity to use probabilistic methods in the evaluation and disclosure of PUD locations that are more than one spacing unit away from existing production. The revisions to the "Modernized SEC Rules" created a need for improved methods to evaluate and disclose probabilistic reserves. The objective of this paper is to present a new and practical probabilistic method for PUD reserve reporting that addresses the SEC requirements. The paper addresses the issues of proved reserve volumes per PUD location, proved well spacing, and "sweet-spots". The useful method may be applicable to a broad range of subjects, such as professional expertise, individual ownership positions, and geologic play situations. The figures and principle steps of the method are presented using the Fayetteville Shale as an example. The method was developed specifically to comply with the Modernized Rules, to be consistent with statistical principles, and to accommodate ownership situations while maintaining an expectation of being practical to use. The first paper ( Dobson 2011 ) in this series of three introduced a new method of describing the proved area in a resource play. The third paper will present a practical justification for this probabilistic approach. In this paper, historical production statistics are used to evaluate the method's ability to comply with reasonable certainty in a forecast at specific PUD locations.

Reserves estimation in an unconventional-reservoir setting is a daunting task because of geologic uncertainty, complex flow patterns evolving in a long stimulated-horizontal well, among other variables. To tackle this complex problem, we present a reserves-evaluation workflow that couples the traditional decline-curve analysis with a probabilistic forecasting frame. The stretched-exponential production decline model (SEPD) underpins the production behavior. Our recovery appraisal workflow has two different applications: forecasting probabilistic future performance of wells that have production history and new wells without production data. For the new field case, numerical model runs are made in accord with the statistical design of experiments for a range of design variables pertinent to the field of interest. In contrast, for the producing wells the early-time data often need adjustments owing to restimulation, installation of artificial-lift, etc. to focus on the decline trend. Thereafter, production data of either new or existing wells are grouped in accord with initial rates to obtain common SEPD parameters for similar wells. After determining the distribution of model parameters using well grouping, the methodology establishes a probabilistic forecast for the individual wells. This paper presents a probabilistic performance forecasting methodology in unconventional reservoirs for wells with and without production history. Unlike other probabilistic forecasting tools, grouping wells with similar production character allows estimation of self-consistent SEPD parameters and alleviates the burden of having to define uncertainties associated with reservoir and well-completion parameters.

Production performance forecasting and the estimated ultimate recovery (EUR) evaluation are two of several uncertainties in the study of tight gas reservoirs. Using the Arps’ decline curve analysis to extrapolate the estimated ultimate recovery (EUR), based on the future production forecasting, is still a widely employed engineering tool in the oil and gas industry today. Tight gas reservoirs usually have a long transient/transitional flow period due to its low permeability. Simply by using the best fit "b" value to estimate the best EUR could generate an error as much as 100% from the true EUR. There has been a number of technical papers published dealing with this issue. This paper will present a new improved methodology to determine a more accurate "b" value to be used in the Arps’ decline curve analysis for tight gas reservoirs. This new approach, using a newly developed relationship between Q cum , Q cum, t →∞ , q t and t, is more rigorous and much easier to use in comparison with all other existing ones. This new improved methodology will not only make future production forecasting more accurate, but also estimate the original-gas-in-place (OGIP) controlled by the well much easier. In Western Canadian Deep Basin, there are many extremely "tight" gas reservoirs that consist of multiple stacked shorefaces. Permeability in each shoreface varies very significantly, generally with the upper shoreface having a better permeability and the middle and lower ones having a permeability as poor as 0.01 mD. By using a reservoir simulator, a number of simulation runs have been performed by the author to answer the following questions: Could Fetkovich's b-values recommendation (mostly b<= 0.4 ~ 0.5 for single gas layer, b>=0.5 for multi-layer without crossflow) still be applied to the tight gas reservoirs? In a multi-stacked shoreface reservoir, does the lower perm zone contribute to the overall production? Moreover, how and when will it influence the production profile? When using the Arps’ decline curve analysis, what are the reasonable b-values to be used for the tight/stacked gas reservoirs? Both synthetic production decline profiles generated from simulation runs and a field example are presented in this paper to illustrate this new proposed methodology and to answer the above three questions.

Recent studies have shown that shale gas industry is incorrectly determining gas-in-place volumes in reservoirs with a large sorption capacity by not properly accounting for the volume occupied by the adsorbed phase. Scanning electron microscopy has discovered nanopores in organic-rich shale with sizes typically in 3–100 nm range; adsorption data show presence of smaller pores and micropores (< 2 nm) as part of the predicted pore size distributions. At pore diameters of this scale the adsorption potential is high and thus the fractional pore volume occupied by adsorbed gas is often substantial. Hence a portion of the total pore volume would be occupied by the adsorbed gas and not available for the free gas molecules. In SPE 131772 a volumetric method, which accounts for the pore volumes occupied by the adsorbed and free gases, has been proposed based on single-component Langmuir adsorption model. In SPE 141416 we recognized the importance and impact of multi-component gas adsorption potential and adsorbed gas density when calculating gas-in-place estimates. We combined the widely used yet thermodynamically inconsistent Extended Langmuir model with volumetrics and free gas composition to formulate a new gas-in-place equation that accounts for the pore space taken up by a multi-component adsorbed gas phase. This paper extends the discussions on the adsorption layer effect of multi-component natural gases. The approach is based on thermodynamically-consistent ideal adsorbed solution (IAS) model to accurately predict adsorbed gas storage capacity for gas mixtures. We expanded on our previous work, where we calculated single-component adsorbed-phase density using molecular modeling and Monte Carlo simulation methods, and propose a new equation-of-state-based analytical approach to predict the adsorbed-phase density of a mixture. In concert, the model improves accuracy of the gas-in-place equations needed to account for the pore space taken up by a multi-component adsorbed phase. The new method yields total gas-in-place predictions, which suggest that an adjustment is necessary in volume calculations, especially for gas shales with high C 2 + composition and high in total organic content. The new method is therefore recommended for shale gas-in-place calculations.

The petroleum industry's lack of understanding of physical properties and the physics controlling production from many important resource plays limits its ability to model and forecast with confidence production and reserves from these resources. While measurement technology and modeling accuracy is improving, the industry is often forced to resort to empirical methods that lack the usual validation required for high confidence in results. This paper analyzes some of the procedures in common use for forecasting and identifies key strengths and limitations of the techniques.

Gas in place/reserves estimation in unconventional (low/ultra-low permeability) reservoirs has become a topic of increased interest as more of these resources are being developed domestically and internationally. Production data from these unconventional gas reservoirs exhibit extended periods of transient/transition flow behavior that often lead to the overestimation of gas in place/reserves with the use of simple rate-time extrapolation techniques. In this work we show that the analysis of production data (particularly rate-time data) before the onset of boundary dominated flow for these unconventional gas reservoirs leads to significant overestimation of gas in place/reserves. Consequently, we introduce the concept of "continuous estimation of ultimate recovery" (or "continuous EUR ") where estimation of reserves from a single gas well is performed in a dynamic fashion — in other words we continuously estimate the reserves for selected time intervals throughout the producing life of the well. We have applied the "continuous EUR " method by using simple rate-time relations such as the Arps' "hyperbolic" rate decline relation and the power-law exponential rate decline relation along with the straight line extrapolation technique, which provides a lower limit for EUR . We present the application of the "continuous EUR " method with a simulated tight gas data set and four (tight gas) field data sets. Our analyses show that the distinction in the flow behavior is more evident and the uncertainty in reserves estimation decreases significantly when reserves are evaluated continuously.

Abstract In recent years, more and more multilayer gas reservoirs were discovered in the world. Forecasting the dynamic reserve of multilayer producer is a major challenge for reservoir engineering. Material Balance Equation (p/z plot) is one of the fundamental methods for reserve estimation. It is usually used in single layer gas reservoir. In this paper, the applicability of multilayer gas reservoir material balance equation for reserve estimation is studied by single well numerical simulation. There are several parameters which influence reserve estimation of multilayer gas reservoir, such as the connectivity between layers of gas reservoir, the difference between formation pressures of the layers, the difference between permeability of the layers. Results of numerical simulation show that vertical permeability is the main factor that influences reserve estimation. Reserve estimation has a large accuracy when vertical permeability is larger than 1E-4mD. Reserve estimation value tends to be smaller than the true value when the vertical permeability is smaller than 1E-4mD, and the more non-homogeneous layer, the larger the deviation is. The results of numerical simulation show that difference between the permeability of the layers is another main factor in reserve estimation. For a larger non-homogeneity between layers, calculated dynamic reserve decreases as increase of non-homogeneity between layers, and the dynamic reserve of the layer with low permeability is low. Introduction The dynamic reserve calculation is one of major tasks for reserve estimation in gas field development. The dynamic reserve is the produced OGIP which is calculated with dynamic method when formation pressure reduces to zero. Calculation methods including material balance method [1,2] , elastic binomial, extended drawdown test, production decline analysis method and numerical simulation, etc. Material balance method is a main method for reserve estimation of gas field. One of the basic assumptions of the method is that gas reservoir consists of single and homogeneous gas tank. Degree of reserve recovery shall be higher than 5% as the methods applies to reserve calculation. Several successful analyses have been performed on single layer reservoir [3] . However, classic material balance equation is unable to accurately forecast production performance of multi-layer gas reservoir [4] . The multilayer gas reservoir can be divided into two types according to whether there is cross flow between layers. One is commingled system which means the layers have been isolated by non-permeable interbed no cross-flow exists, the other is crossflow system where there is connectivity between layers [5] .

Abstract Decline curve analysis using the Arps rate-time equations has historically been the primary tool used for reserve evaluations in tight gas sands. However, characteristic tight gas sand properties often preclude accurate assessments using only or primarily decline curve analysis — especially early in the productive life. Moreover, the Arps models are essentially used as curve-fitting devices with (very) limited theoretical basis. As such, this paper presents a reserves appraisal process that complements traditional decline curve analyses with theoretically-based production analysis techniques. Specifically, we incorporate multiple reserves estimation techniques ranging from simple extrapolation techniques to rigorous model-based analysis/interpretation methods, including: Arps exponential and hyperbolic relations — rate-time and rate-cumulative plots, as well as other auxiliary plots, Semi-analytical rate-cumulative techniques for boundary-dominated flow, The "flowing material balance" approach in its various formulations, and Model-based analysis ("rate-transient" or "model-matching" approaches). We provide both separate and combined analysis, including "diagnostic" plots which guide the evaluation process and help prevent erroneous reserve estimates resulting from use of single methods. Implementation of the proposed workflow will prevent unrealistic (either too low or high) reserve bookings by integrating multiple production analysis techniques. We demonstrate where and how each component technique should be used as well as their limitations. We also demonstrate how each technique can be used to reinforce/validate results from other techniques ( i.e. , multiple redundancies of methods). The primary objectives of this work are to: Provide a consistent workflow for integration of multiple reserve estimating techniques for tight gas sands, Provide an assessment of strengths and weaknesses of individual reserves estimation techniques, and. Provide demonstrative examples for implementing this workflow (including diagnostics).

Abstract Material balance analysis is a fundamental technique for estimating gas-in-place. It can be achieved using: Static material balance, using static (shut-in) reservoir pressures, where a plot of static p/z versus cumulative gas production is created to estimate original-gas-in-place (OGIP) or Flowing material balance where gas rates and flowing pressures are used to estimate average reservoir pressure. The flowing material balance concept of Agarwal-Gardner (1999) was extended to dry coalbed methane (CBM) reservoirs by Clarkson et al. (2007a) and Gerami et al. (2007) and to 2-phase (gas and water) CBM wells by Clarkson et al. (2007b). The present study further enhances the flowing material balance for dry CBM reservoirs by presenting a p/z* implementation of the concept. This application, while accounting for the distinguishing characteristics of a CBM reservoir, uses the industry-standard practice of p/z material balance to calculate original-gas-in-place. As with the Agarwal-Gardner approach, the flowing p/z* method can be applied to variable gas rates and/or flowing pressures conditions. In the present work, the derivation and iterative procedure of calculations are explained. Several test cases based on dry/immobile water saturation using real and synthetic data were generated. The resulting estimates of OGIP calculated from implementation of flowing p/z* material balance show excellent agreement and the estimated OGIP's are reliable. Introduction Material balance is the application of mass balance to a producing reservoir. As gas is produced, the reservoir pressure declines. By monitoring the cumulative gas production and the average reservoir pressure, and using the PVT properties of gas, one can determine OGIP and the remaining gas-in-place. Material balance analysis, although simple and more reliable than volumetrics calculation, does suffer from a number of shortcomings and limitations. For example, to measure the average reservoir pressure the well has to be shut-in and that means loss of production. Among other complexities are: low permeabilities lead to poor pressure build-ups (long-buildup times required) pressure build-up can be masked in multiple coal seams reservoir can be recharged from aquifers CBM reservoirs have additional complexities. The gas storage mechanism as well as the compressibility of CBM reservoirs is dominated by adsorption. These and other CBM-specific characteristics have to be accounted for in any material balance calculations.

Abstract Despite its many limitations, conventional decline analysis is still commonly used in gas production analysis due to its minimal data requirements and ease of application. Yet, there is strong desire and need for more sophisticated methods such as those loosely defined by the term advanced decline analysis. As a result, this paper presents an automated computer method for estimating original gas-in-place and other reservoir flow parameters. The method is semi-analytical in that it is based on a semi-empirical relationship between gas production and initial reservoir conditions, but is coupled with conventional decline analysis methods for ease adaptation into the workflow. The procedure provides additional information comparable to flowing material balance analysis, but within the familiar Arps decline environment - a critical component that makes the procedure very amendable to traditional workflows. Another advantage is that the proposed methodology does not require a prior knowledge of net pay, formation and/or fluid compressibility, or reservoir pressure (other than initial conditions) for estimation of OGIP. Results will also show that the approach is generally accurate and robust (when used appropriately) and provide additional information such as effective permeability if a few volumetric parameters are provided. And that the procedure is extremely simple and can be implemented in desktop applications or spreadsheets with minimal computational effort. Although there are limitations such as constant flowing pressure (as well as multiphase flow), it will be demonstrated that the procedure is generally versatile and can be applied to a number of gas systems including conventional sands and silts, thermogenic and biogenic shales, as well as (dry) coal bed methane. Results and observations are based on both simulated and field examples for a variety of operating conditions. In conclusion, a novel and rapid methodology has been introduced for estimating original gas-in-place, ultimate recovery (EUR), and other flow parameters with the simplicity of conventional decline. Introduction Decline curve analysis has been a widely used method for forecasting future production from oil and gas fields since Arps 1 formularized the technique, particularly for single phase flow. In fact, the procedure can be applied to production data from any type of reservoir drive or mechanism: allegedly Fetkovich 2 actually established guidelines about how curves can indicate different reservoir systems. However, despite its simplicity, analysts typically want more information without having to incur more time consuming processes; for example, they are interested in permeability, formation damage (wellbore skin), EUR, as well as OGIP. Furthermore, there is a desire to use the aforementioned information to generate production forecasts, and evaluate varying operating conditions. As result, Fetkovich 2 later proposed a substantial improvement in decline curve analysis by matching production data onto specialized type-curves for reservoir characterization. Al-Hussiany 3 later improved type curve analysis by the introduction of mechanisms for addressing the effects of pressure dependant gas properties viscosity and supercompressibility through the use of pseudo-pressure, while Agarwal 4 presented a pseudo-time function for incorporating pressure dependant properties gas viscosity and compressibility. Another type curve solution was presented by Carter 5 who correlated the case of a gas well produced at constant bottomhole pressure using specialized dimensionless variables. Modern methods such as those of Blasingame 6 and Argawal-Gardner 7 are similar to Fetkovich in that they use type curves for production data analysis. However, they use flowing pressure data, combined with analytical solutions, to evaluate hydrocarbons-in-place. Another highly popular method is the flowing material balance (FMB) :a procedure in which variable rate-pressure data can be normalized for a linear extrapolation to fluids-in-place. Other methods such as those presented by Ibrahim et al 9 show tangent methods for evaluating tangents for OGIP using superposition time functions.

Abstract The evaluation of unconventional resources presents many challenges, one of which is a need to evaluate all resource classes, not only Proved reserves. The evaluation of hydrocarbons is described as consisting of three steps: Estimation, Classification, and Reporting, with a concomitant requirement for auditing. The nature of estimates is discussed, in particular, that there is always an associated uncertainty and some fundamental principles of classification are reviewed. The classification of unconventional hydrocarbons is discussed, with an emphasis on Discovered Petroleum Initially-In-Place and Contingent Resources, which are formulated as a series of Decision rules. Information on production potential, either from tests or analogs, is of particular importance when classifying unconventional hydrocarbons, and the typical lack of defined pool boundaries requires that careful consideration be given to the area assigned to various resource categories, both of which points emphasise the need for an understanding of the detailed reservoir geology. The Petroleum Resource Management System (PRMS), supplemented by the Canadian Oil and Gas Handbook (COGEH), has provided the basis for the discussion in this paper, but it is impossible in a short paper to capture these systems in full, and reference should be made to the original source documents for details. The emphasis in this paper is on recovery through wells, but some brief mention of mined hydrocarbon recovery is made. The exploitation of unconventional hydrocarbons is in its early stages, and evaluation procedures, including classification, are still under development. Many aspects of the evaluation process are not addressed in this paper, and those that are, are likely to require further development and modification. Introduction Rapidly increasing exploitation of so-called unconventional resources and a demand to evaluate categories of resource in addition to Proved (or sometimes Probable) reserves present new challenges to the evaluator. The most obvious challenge is the technical one of understanding the geology and production characteristics of new types of reservoir. Identification of the presence of an unconventional hydrocarbon accumulation through drilling, logging, and coring, is usually relatively straightforward. The determination of the production characteristics and of commercially viability is not straightforward, since it requires more extensive testing than for a typical conventional accumulation. The productive life of these accumulations is often long, and the development process is complex. The transition from other resource categories to reserves can be slow, and limiting the evaluation to only reserves fails to present an adequate picture. The need to assess other categories of resource than Proved (and sometimes Probable) reserves raises significant questions, especially on classification.

Abstract SPE has long been a leader together with several other cooperative industry organizations over time in establishing petroleum resources and reserves definitions. This began in 1962 with the appointment of the first SPE Oil and Gas Reserves Committee (OGRC) and continues through the most recent SPE programs and processes created to inform and educate industry and the public about the applicability and significance of the Petroleum Resources Management System (PRMS) approved in March 2007. The three other sponsors of the PRMS are the World Petroleum Council (WPC), the American Association of Petroleum Geologists (AAPG) and the Society of Petroleum Evaluation Engineers (SPEE) who, along with SPE, effectively represent virtually every segment of the greater petroleum industry worldwide. Developing the PRMS required more than three years and the direct, hands-on participation of more than two dozen industry volunteers. The document was further subjected to a 100-day industry wide review period where comments were solicited and incorporated, as appropriate, into the final approved version. This was, in every measure, a global effort as the mandate for the OGRC and its observers was strengthened by having representation from 10 countries including Australia, Canada, Hungary, Italy, Norway, Saudi Arabia, South Africa, United Kingdom, United States and Venezuela. Employers of the OGRC members and those of its six observers included privately-owned, state-owned, independent and integrated producers plus large and small consulting firms, one government agency and an international accounting board. All of the individuals who participated in the creation of the PRMS fully recognize the dynamic nature of this exercise and the continuing need for periodic updating of the constituent PRMS documents in recognition of advancing technology and ongoing global discussions. The value of having a unified (and broadly understood) set of definitional standards for petroleum resources definitions will become more appreciated over time as many world leaders continue to express concern about the ability of the industry to meet its share of the energy demand of an expanding population and economy. Many would agree that the first concrete step in achieving international recognition of a single standard for petroleum reserves and resources definitions began with the efforts of the WPC and SPE in their joint 1997 publication of updated reserves definitions and the recognition of both deterministic and probabilistic assessment methodologies. Three years later, the AAPG joined SPE and WPC in developing and issuing petroleum resources definitions in 2000. These two significant steps plus the inclusion of the SPEE led to the March 2007 release of the PRMS. The more challenging part of this process clearly lies ahead as the sponsoring organizations work together to "sell" these definitions and principles to the upstream petroleum industry management, the financial community including investors and creditors, the accounting world, governments, certain mining interests and governmental regulators. Upstream investment decisions, including those arising from cooperative agreements between companies, are facilitated and optimized through reliance upon information generated through a uniform and modern set of resource definitions and classification standards. Investors, in particular, need to have a clear understanding of the meaning of terms in a reserves report, for example, before they can make judicious decisions about value and comparability.

Abstract Modern computing power has enabled very high accuracy and efficiency in complex calculations. Thus, it follows that reservoir engineering formulations need not be approximate solutions, as was sometimes historically the case. Being one of the most widely used techniques in reservoir engineering, the material balance equation (MBE) for gas is an excellent example of this. The MBE is used not only for estimating the original gas-in-place (OGIP), but also for calculating the decline in average reservoir pressure with depletion. In the majority of cases, the conventional p/Z formulation of the gas material balance is satisfactory. However, certain circumstances, which are sometimes unpredictable, demand formulations with greater accuracy. Although modifications to the standard approach have been presented, to the authors' knowledge, there is no published gas material balance formulation that is completely rigorous. This study presents a new, rigorous MBE for gas flow in the presence of a compressible formation and residual fluid saturation. Examples will be presented to highlight the capabilities of the new MBE. Introduction There has been a demand in the petroleum industry to plot the p/Z (or a similar plotting function) vs. cumulative gas production ( Gp ), as accurately as possible, so that the extrapolated line can point to the correct amount of OGIP. The primary value of this approach is to calculate the average reservoir pressures from the MBE. This has been one of the most powerful tools in reservoir engineering. Conventional MBE[1] for gas flow from a "volumetric" reservoir assumes the available pore volume to gas is constant by disregarding the effect of formation compressibility and the expansion of residual fluid during the productive life of the reservoir. Such assumptions may not be acceptable where the rock compressibility is of the same order of magnitude as residual fluid or gas. Ramagost and Farshad[2] modified the classical material balance equation and proposed a new plotting function as [( p/Z ) (1- ce ?p )] vs. Gp, based on an improved version of the conventional MBE. On a number of occasions with certain combinations of compressibility and saturation values, it has been observed that the Ramagost and Farshad MBE should have been more accurate in predicting the average reservoir pressure. We have examined how this MBE was derived. As will be shown later, the contributions of the shrinkage of the pore volume and of the expansion of residual fluid saturation were approximated in this approach. With the advent of computer power now-a-days, the accuracies of reservoir engineering calculations are warranted. The material balance equation (MBE) for gas flow has been used not only for estimating the OGIP, but also for estimating the average reservoir pressure. In addition, accuracies of MBE have to keep pace with the results from sophisticated reservoir simulations (both analytical and numerical). The following are the major applications of an MBE: To estimate average pressure for a given cumulative gas production. To estimate the cumulative gas production for a given average reservoir pressure. To estimate the OGIP from a given history of static pressure vs. cumulative production. To calculate pseudo-time[3] when using analytical solutions developed for liquids for modeling gas reservoirs. This study presents a new, rigorous MBE for gas flow in a compressible formation with residual fluid saturation. A new dimensionless parameter in the MBE will be identified and explained how this can predict the behavior the MBE. Examples will be presented to highlight the capabilities of the new MBE. The principal assumptions remain identical to those of Ramagost and Farshad[2] as compressibilities of rock and residual fluid are constant. Presentation of New MBE In the following sub-sections, we present the new, rigorous MBE, its implications and an explanation of the methodology of calculating the average reservoir pressure from it.

ABSTRACT Accuracy in hydrocarbon reserves estimates affects virtually every phase of the oil and gas business. Unfortunately, reserves estimates are uncertain, since perfect information is seldom available from the reservoir, and uncertainty can complicate the decision-making process. Managers have to make many important decisions early (e.g., facilities expansions, development drilling, etc.) without reliable knowledge of reserves. Thus, it is probably more important to quantify reserves uncertainty early than any other time in the life of a reservoir. Reserves are closely related to original hydrocarbons in place (OHIP). Two methods for estimating OHIP are volumetric and material balance methods. The volumetric method is convenient to calculate OHIP during the early development period, while the material balance method can be used later, after some performance data, particularly pressure and production information, are available. Both methods may have substantial uncertainty. In this paper, we present a methodology that uses a Bayesian approach to quantify the uncertainty of original gas in place (G), aquifer productivity index (J), and the volume of the aquifer (Wi) by combining volumetric and material balance analyses in a water-driven gas reservoir. The results show that we potentially have large uncertainty in OGIP estimates when we consider only volumetric analyses or only material balance analyses. However, by combining the results from both analyses, the uncertainty can be reduced. This reduction in uncertainty should lead to better management decisions in many cases. INTRODUCTION The volumetric method is useful in calculating hydrocarbon reserves prior to availability of representative pressure and production data.[1] This method uses static reservoir properties such as area of accumulation, pay thickness, porosity, and initial saturation distribution. Given the often large uncertainty due to paucity of well data early in the reservoir life, it is common to quantify the uncertainty of volumetric estimates of OHIP using statistical methods such as Monte Carlo analysis. The material balance method can be used later when sufficient amounts of pressure and production data are available. The material balance method is simply an inventory of all materials entering, leaving, and accumulating in the reservoir. Since it relies on different data from the volumetric method, the method can be used as an independent check of volumetric estimates of initial hydrocarbon volumes in place in a reservoir. If the material balance method is properly applied, it can be used to estimate initial hydrocarbon volumes in place, predict future reservoir performance, and predict ultimate hydrocarbon recovery under various types of primary-drive mechanisms. Although uncertainties in material balance methods have been long recognized, they are often considered more accurate than volumetric methods, since they are based on observed performance data. It is not common practice to formally quantify the uncertainty in material balance estimates of OHIP. Bayes' theorem[2–4] provides a mathematical basis for revising preliminary estimates of reservoir characteristics and their uncertainties when additional information becomes available. Floris et al. [5] applied Bayes' theorem to quantify uncertainty in production forecasts from reservoir models conditioned to both static and dynamic reservoir data. Glimm et al. [6] showed that the Bayesian approach can reduce the uncertainty in the prediction of unknown geological parameters in the simulation of an oil field. Galli et al. [7] used the Bayesian approach to evaluate new information for choosing between different exploitation scenarios for a gas field. Ogele et al. [8] used the Bayesian approach to combine volumetric and material balance methods and quantify uncertainty of OHIP estimates in gas-cap driven oil reservoir. They quantified the uncertainty of two parameters, original oil in place and relative gas-cap size, estimated using the Havlena and Odeh form of the material balance equation.

Abstract This paper presents simple and practical equations to estimate a priori the decline-exponent for decline-curve analysis of gas wells. The proposed equations are applicable for gas wells with closed boundaries and constant rock properties. Data required includes initial reservoir pressure, bottomhole flowing pressure, and fluid properties. These data are that required in a typical decline-curve analysis (i.e., no extra data is required). A field example is presented to verify the developed concept and to demonstrate the utility. Introduction Two practical issues arise in applying the "Fetkovich-Arps type" of type-curves. 1–4 First, it is very difficult to match a decline exponent at early depletion stage even with good quality data (e.g., around the region of t dD 1 in the type-curve of Fig. 1 5 ). Unfortunately, this is also the time (early depletion) when some key answers (e.g., gas-in-place) are needed most for a future planning. Poor data quality (e.g., scattering) makes the situation even worse. Second, the decline exponent, strictly speaking, is not a constant. (We will show examples later.) Rigorous handling the variable decline exponent is possible. Such an avenue, however, will offset significantly the advantages of using the Fetkovich-Arps approach. An estimated most-likely or optimal decline exponent before data analysis is desirable to guide the type-curve matching, and to relieve the previously mentioned two difficulties. Future rate-time prediction and fluid-in-place estimation then can be made more confidently and as early as possible. The objective of this study, thus, is to develop a method to estimate or predict the decline exponent of gas wells before data analysis. Instantaneous Decline Exponent The instantaneous decline rate, D , is defined as the fractional instantaneous change in flow rate with time, 6,7 Equation 1 The instantaneous decline exponent, b , is defined as the instantaneous time-rate change of the inverse of D , 6,7 Equation 2 The definitions of D and b given above are general, valid for any time and any type of flow. The unit of D is that of the reciprocal of time while b is dimensionless. The case of b = 0 (or equivalently, D = constant) would result in exponential decline whereas cases of b > 0 give hyperbolic decline. Based on gas inflow equation during boundary-dominated flow period and material balance equation, Appendix A derives the following theoretical expressions (pseudopressure, p p , and p -squared, p 2 , approaches) of post-transient instantaneous b for constant BHP production, Equation 3 where n is the back-pressure curve exponent. Other notations are defined in the Nomenclature. Pore volume was assumed to be constant in deriving Eq. 3. Pseudopressure-Based Instantaneous b . Eq. 3 indicates that the instantaneous b , in general (pp-approach), is not a constant; b reflects the change of viscosity-compressibility product µ g c t as the reservoir pressure changes. Specifically, b is the slope of ln(1/µ g c t ) vs. ln[ p p ( p R )- p p ( p wf )]. If the slope is positive and constant, b is a positive constant and the Arps' decline equations are valid. b is zero only if the µ g c t product is constant. Appendix B shows that the post-transient rate-time equation (constant BHP) is exponential if the variation of µ g c t (and V p if necessary) can be taken into account in the time scale. Pseudopressure-Based Instantaneous b . Eq. 3 indicates that the instantaneous b , in general (pp-approach), is not a constant; b reflects the change of viscosity-compressibility product µ g c t as the reservoir pressure changes. Specifically, b is the slope of ln(1/µ g c t ) vs. ln[ p p ( p R )- p p ( p wf )]. If the slope is positive and constant, b is a positive constant and the Arps' decline equations are valid. b is zero only if the µ g c t product is constant. Appendix B shows that the post-transient rate-time equation (constant BHP) is exponential if the variation of µ g c t (and V p if necessary) can be taken into account in the time scale.

Abstract Straight-line material-balance plots are commonly applied to analyze reservoir performance and estimate the OHIP (original-hydrocarbon-in-place). Depending on the type of the straight-line plot is applied the OHIP may be found either from the slope or the intercept of the straight line. In the case of a gas-condensate reservoir, the compositional effect and the overpressure effect must be formulated into a general material-balance equation to obtain a valid straight-line plot. For obtaining a correct OHIP a line must be both linear and having the correct slope or intercept when straight-line material-balance plots are used. Unfortunately, a quality material-balance evaluation for a gas-condensate reservoir is difficult to achieve using straight-line material-balance plots because the additional data required for calculating the compositional effect and the overpressure effect are difficult to obtain. This paper illustrates that the compositional effect and the overpressure effect can be ignored if the results obtained based on a general material-balance equation are plotted as a horizontal line. In horizontal line material-balance plot no slope or intercept is used as the condition for determining the OHIP because only a correct OHIP can give a horizontally linear material-balance plot, the only condition to meet. Horizontal-line material-balance plots simplify the evaluation procedure and improve the quality of material-balance valuations. Introduction Material-Balance (MB) allows for evaluating the original-hydrocarbon-in-place (OHIP) by analyzing the field production performance. In cases of gas-condensate reservoirs the general material-balance equation (GMBE) written for MB evaluations must be modified to account for the compositional effects as that reported by Wang 1 . Recently, a material-balance evaluation approach that does not require any aquifer model was proposed by Hsieh et al 2 . In that approach (Direct approach) instead of independently calculating water influx terms (Aquifer Model approaches) then substitute them into the material-balance equations, they reverse the procedure to directly calculate water influx terms from the material-balance equation assuming the OHIP is known. Such a process simplifies the material-balance evaluation significantly because calculating water influx with known (assumed) OHIP is far simpler than that of using aquifer models. When using aquifer models not only the selected aquifer model must be able to adequately represent the actual reservoir-aquifer configuration but also the properties of the aquifer must be available. Usually, a unique solution for OHIP is difficult to obtain when using aquifer models. This happens because the aquifer model approaches attempt to solve material-balance equations based on a material-imbalance procedure in that the water influx terms are independently calculated (predicted) using aquifer models that rarely represent the true and unique reservoir-aquifer system. In applying Hsieh's Direct approach, a series of water influx index values are directly calculated for a series of assumed OHIP's using only the general material-balance equation (GMBE). Therefore, in the Direct approach the materials (terms) in material-balance equation are always balanced. Once the influx terms are solved, they use horizontal plot of Water-Influx-Index vs. Time (C-T plot) instead of the traditional linear material-balance plots to determine the OHIP. That is, the assumed OHIP that yields the most horizontal line is taken as the final OHIP because each aquifer has its unique influx index (rb/d/psi) just like each reservoir has its unique productivity index. In a C-T plot a volumetric reservoir has a consistent C of zero (or small) at different time steps, otherwise, a horizontal line with C values greater than zero (positive and not negligible) appears, indicating aquifer influx. This paper shows the Direct approach is also effective for evaluating a gas-condensate reservoir. Review of Direct Approach The formulation of Hsieh's Direct approach can be briefly explained using the following equations, taken from their paper 2 .

Abstract This paper presents results from evaluations of geochemical, petrographic and reservoir properties of Powder River Basin, Paleocene Fort Union Formation Canyon seam subbituminous coal at the Triton Federal #21C-2623 coalbed methane well in Campbell County, WY. The core analysis data reveal that at this location the Canyon seam coalbed is a multi-layered reservoir with each layer having different bulk compositional and fluid mobility properties. A very unique reservoir property of this subbituminous coal is its extremely fast gas desorption rate, which, in turn, facilitates a high early time gas production rate from the coalbed reservoir. The core analysis data indicate that the gas desorption rate is dependent on the maceral composition of the bulk coal organic matter. It was found that several commonly used coal and coalbed reservoir property analysis methods required modification to prevent or minimize errors due to subbituminous coal's propensity to undergo aerial oxidation and desiccation. The occurrence of these processes during sample handling, storage or testing causes significant (15% or greater) underestimation errors in key reservoir properties such as the in-situ gas content, moisture holding capacity, sorbed gas composition, and percent gas saturation. Effective methods for avoiding or minimizing subbituminous coal property analysis errors due to oxidation and desiccation are described in this paper. Because these error prone analysis practices have been commonly used in the past, there may be significant potential for large coalbed methane resource and reserve estimate gains in this and other Rocky Mountain region basins. Introduction The Tongue River Member of the late Paleocene Fort Union Formation in the Powder River Basin contains thick beds of subbituminous coal, which are reservoirs for an estimated 24 Tcf (0.68 × 10 12 m 3 ) of coalbed methane resources. 1 The U.S. Potential Gas Committee has estimated that the Powder River Basin contains 9.33 Tcf (0.26 × 10 12 m 3 ) of recoverable coalbed methane resources. 2 A major play area for the commercial production of these coalbed methane resources is located along the eastern flank of this basin, down-dip from large surface coal mines in Campbell County, Wyoming. 1–6 In 1998, coalbed methane production from this play totaled 27.8 Bcf (0.79 × 10 9 m 3 ) from about 650 wells. 7 The coalbed methane wells in this play are relatively shallow, typically only 250 to 1,000 ft deep (76 to 305 m), cost only about $50,000 to drill and complete, and have initial gas production rates ranging from less than 100 up to 1,000 Mscfd (2,832 to 28,320 m 3 /day). 1–6 The use of low-cost, innovative drilling and completion practices have been critical factors in making this coalbed methane play economical. 1,4–6 The average gas reserve finding cost in this coalbed methane play is about $0.25 per Mscf (28.32 m 3 ) which compares favorably with average reserve finding costs of from $0.15 to $0.50 per Mscf (28.32 m 3 ) for conventional on-shore natural gas resources. 7,8 The water production rates from these shallow coalbed methane wells average about 330 STB (52.5 m 3 ) per day. However, the water disposal cost is generally minimal because the coproduced water quality is relatively good, typically less than 2,500 ppm total dissolved solids, enabling beneficial uses or direct discharge, under permit, into surface drainages. 1,3–6 This coalbed methane play is quite unique because several coal properties are strikingly different from those of commercially developed coalbed methane plays in other U.S. basins. 7,8 In particular, the coal rank is much lower, the gas content is typically an order of magnitude lower, the coal moisture content is typically an order of magnitude greater, and the absolute water permeability is typically an order of magnitude greater, ranging from tens of md to more than a Darcy. 4,5