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UNCONVENTIONAL ESTIMATION OF OIL AND GAS RESERVES USING PRODUCTION RATES DECLINE TRENDS ANALYSIS
ABSTRACT
Unconventional (projectile and parabolic), methods have been used to estimate oil and gas reserves. Existing oil and gas data from wells in the Niger Delta geological formations (Agbada, Akata and Benin) were used to generate decline constants ‘b’ that were subsequently used in predicting yearly production data for any given period. The yearly data obtained were validated using the actual yearly production records of the original data source. The validated yearly data were used to generate evaluation curves. The evaluation models were subsequently worked out from the shape of the generated curves. The models were then used to estimate reserves (cumulative and initially in place) in each of the reservoirs. The values obtained compared favorably with the respective storage tank and the volumetric materials balance equations values. The percentage accuracy for gas fields ranged from 99.86% and above, while the percentage accuracy for oil ranged from 98.64% to 99.98%. The results of this research simplifies complex simulation methods, improves dynamic fluids computational analysis, reduces time in the conventional decline analysis and makes it easy to identify dominated flow and rates decline trends. The models are very flexible and can be applied with high accuracy from the reservoir decline stage to abandonment. They are equally used to estimate the remaining reserves based on the time differences between final and production ( tf-t p) and for the establishment of production and economic decisions techniques.
TABLE OF CONTENTS
Title Page i
Certification ii
Dedication iii
Acknowledgments iv
Abstract v
Contents vi
List of Tables viii
List of figures ix
Nomenclature xii
Chapter 1: Introduction 1
1.1 Background Information 1
1.2 Problem Statement 5
1.3 Objectives 6
1.5 Justification of Study 7
1.6 Scope of the Study 7
Chapter 2: Literature Review 8
2.1 General Field Records on Production Decline Rate 8
2.2 Simulated Production (Generic) Data 9
2.3 Constant or Exponential Decline Rate 10
2.4 Hyperbolic and Harmonic Decline Rate 11
2.5 Values of Rate Decline Range 14
2.6 The Power Law Decline Rate Constant Method 15
2.7 Concept of Integral Type Curves 18
2.8 Fractional Hydrocarbons Decline Rate 21
2.9 Fractional Decline exponent (n), Obtained Conventionally 24
2.10 Natural Reservoirs Hydrocarbons Production Decline 26
2.11 Decline Rate correlation as a function of Time 27
2.12 Well Production Performance 30
2.13 Relative Decline Rate 31
2.14 Reviewed Evaluation and research Proposal 38
Chapter 3: Methodology 39
3.1 Materials for the research 39
3.2 Research Methodology 39
3.3 Analysis Procedures 43
3.31 Postulation of the Projectile Models 45
3.32 Postulation of the Parabolic Models 53
3.33 Hydrocarbons Production Models
58
3.4 Cumulative Hydrocarbons Production Models 63
3.5 Hydrocarbons Initially in Place (G or N) Postulation 68
3.6 Projected Hydrocarbons Production Models 76
3.61 Application of the Model Equations Using Regional Data 76
3.62 Application of the Evaluation Models Using Generic Data 80
Chapter 4: Results and Discussion 91
4.1 Results 91
4.1.1 Evaluation Model – 1: The Projectile Gas and Oil Flow 91
4.1.2 Evaluation Model – 2: The Parabolic Fluid Flow Regime 93
4.1.3 Cumulative Hydrocarbons Production Models 95
4.1.4 Projectile Model Equations Application Results 98
4.1.5 Model Equations Application Results Using Generic Data 101
4.2 Discussion 112
4.2.1 Projectile Dominated Fluids Flow Regime 112
4.2.2 Parabolic Fluid Flow Regime 114
4.2.3 Cumulative Hydrocarbons Production Models 117
4.2.4 Parabolic Flow with no Observable Transient or Transition
118
4.2.5 Application of the Model Equations Using Generic Data 119
Chapter 5: Conclusion and Recommendations 121
5.1 Conclusion 121
5.1.1 Contributions 123
5.2 Recommendations 125
References 126
APPENDIX – A: Volumetric MBE for Models Validations 131
APPENDIX – B: Fields Evaluation and Development Models (FORTAN 77) 138
CHAPTER 1 INTRODUCTION
1.1 Background information
Decline curve analysis are mathematical equations, tabulated values or graphical procedures for studying the oil and/or gas production rates, prediction of cumulative oil or projected oil production and estimating the oil or gas initially in place. A field production history is used in projecting future hydrocarbons production rates in a given time. The projected rates are plotted against time, used in the prediction of future production and the initial oil or gas reserves. In some cases standard curves are used for comparison. These standard curves were obtained using field data (called regional data). The curve fit is then extrapolated to predict oil or gas reserves. Decline curve is the basic tool for estimating the recoverable reserves. Conventionally, decline curves analyses are only possible when the production data or history is available, so that the trend can be defined. There are no fundamental theoretical trends for decline curves analyses, but the exercise is based on production data trend. For this the principal challenge is to minimize errors. All data must be understood before use. There are three principal types of decline rate as postulated by the early researcher. These are exponential or constant decline rate, harmonic decline rate and hyperbolic decline rate. This classification is based on constant or variable changes in the factors that influence the fluid flow in a porous medium. Crafts and Hawkins, (1959) stated these factors as follows:
- Constant well back pressure effects ii. Active water-drive influences iii. Boundary conditions in a porous zone iv. Historical observation of the data
- Single or multiple phase fluid flow vi. Combined oil and gas flow as a stream
The equation of a fluid flow through porous media under boundary conditions is based principally on steady-state, semi-steady state and unsteady-state and are applied as deemed fit for any particular situations single or two phase fluid stream. Oil as a single stream can only be mobile if gas is dissolved in it and oil and water combined as a multiphase fluids stream with gas dissolved in the stream for mobility effect. Any stream can exhibit any type of decline rate. It depends on the influencing factors. The analysis can be conducted on only one fluid stream or a combined fluids stream gas oil ratio (GOR). The practical approach to oil or gas production decline rate analysis is to choose the variables such as gas or oil stream that results in a reasonable trend. The decline rate curves are used to predict the future well performances. The accuracy in predicting the future oil or gas stream performances depends on the ability to understand the reservoir characteristics and the standard established for estimating the reserves. In decline curve analysis it is implicitly assumed that factors causing the historical decline in a fluid stream would continue unchanged throughout the forecasting period. Crafts and Hawkins, (1959) field records showed that these factors are the reservoir and operating conditions.
a. Reservoir Characterization
- Reservoir drive mechanisms ii. Saturation and viscosity changes iii. Permeability and its distribution iv. Porosity and its distribution
- Volumetric mobility of the fluids vi. Formation grain sizes and arrangement
b. Operating Conditions
- Fluids flow mechanism ii. Pressure depletion trend iii. Decline rate trend iv. Tubing and choke sizes
- Number of producing wells vi. Separation pressure and its operating hours vii. Work-over jobs effects viii. Compressors operating hours
- Artificial lift effects
In analyzing rate of decline, two primary types were used. The flow rate was plotted against time to predict projection rates and the daily oil or gas production was plotted against time to estimate future cumulative production and reserves originally in place. The most convenient dependent variable is the rate, because extrapolation of the rate-time graph was used directly to forecast the fluid production and economic evaluations. Plots of rate against daily oil or gas production equally provided direct ultimate recovery at a given economic limit and yielded a more rigorous interpretation where the production was influenced by intermittent operations. In this case best rate decline trends analyses were compared with volumetric calculated values, MBE values and recovery factor values. The decline curves analysis results were the estimation tools for the cumulative hydrocarbons production and hydrocarbons initially in place which are fixed in nature. Field records by Crafts and Hawkins, (1959) showed that recoverable hydrocarbons are affected by the operating conditions, decline curves analyses are best applicable when the production stabilizes, because of boundary condition dominated flow rate. Most decline curves analysis states that evaluation starts with stabilized flow decline rate. Another school of thought states that decline curves analysis is based mainly on empirical observation of production rate decline and not on theoretical derivation. Any attempts to explain the observed behaviour using a theory of fluid flow in porous media would require the boundary dominated flow relationship. When a well is placed on production, there will be transient flow initially, because the boundary conditions are not active enough. Eventually the reservoir boundaries would be felt and it is only then that decline rate becomes clear and the value of the decline rate constant (b) lies between 0.0 and 1.0 or higher, depending on the reservoir boundary conditions and drive mechanism. Occasionally the decline rate has a value greater than unity. It is very useful to have production decline rate model in the Niger Delta and other fields in order to predict projected production rates and estimate both reserves in place and the recovery factor in a reservoir. This equally defines the production decline trend and the process that starts a transient state, peak and decline to minimum level or economic limit rate called abandonment rate. The decline models would enable a prediction of the recovery efficiency profile, gives the investors much knowledge of his business profile or trend.
1.2 Statement of the Problem
Many reserves are abandoned early, because of complex simulation procedures in order to establish motivated economic techniques. Conventionally, volumetric material balance equations (MBE) methods in use are limited to static conditions of the reservoirs and less accurate in the dynamic fluids computation analysis. Equally conventional decline analysis is less accurate, because most researchers assumed exponential or constant rates decline. In reality some reservoirs are not. In this work, mathematical equations or relationships are developed to increase DFCA accuracy and
discourage early or premature abandonment of reserves (ref: results in chapter 4).
1.3 Objectives
The main objective of this study is to derive more accurate
mathematical rates decline relationship to predict oil and gas reserves. The specific objectives in order to achieve the above aim are:
- to validated the derived relationship for selected reservoirs, using storage tank records.
- to correct already existing relationships, using the validated relationships.
- to economically improve the methods for easy and correct
identification of production rate decline trends.
- to improve the evaluation models quality and results accuracy.
1.4 Justification of Study
This research work is necessary to simplify the complex simulation procedures in the conventional methods for rate decline analysis. This would increase DFCA accuracy, reduce the simulation complexity and time used. The success of this work will give an investor the view of his business and it improves his decision on the business.
1.5 Scope of the Study
This work primarily covers production decline rates characterization for some oil wells in the Niger Delta. The collated data covered the unsteady-stage (early-stage), steady-stage and semi steady-stage (decline-stage) of a reservoir. The complete production data to abandonment can be used for mathematical equations derivations and confirmation. The decline stages data covered the declined constant estimation and applications. The data in the short period production took care of the projected reserves recovery estimation and time required.
UNCONVENTIONAL ESTIMATION OF OIL AND GAS RESERVES USING PRODUCTION RATES DECLINE TRENDS ANALYSIS
