This paper presents a methodology for connecting geology, hydraulic fracturing, economics, environment and the global natural gas endowment in conventional, tight, shale and coalbed methane (CBM) reservoirs. The volumetric estimates are generated by a variable shape distribution model (VSD). The VSD has been shown in the past to be useful for the evaluation of conventional and tight gas reservoirs. However, this is the first paper in which the method is used to also include shale gas and CBM formations.. Results indicate a total gas endowment of 70000 tcf, split between 15000 tcf in conventional reservoirs, 15000 tcf in tight gas, 30000 tcf in shale gas and 10000 tcf in CBM reservoirs. Thus, natural gas formations have potential to provide a significant contribution to global energy demand estimated at approximately 790 quads by 2035.
A common thread between unconventional formations is that nearly all of them must be hydraulically fractured to attain commercial production. A significant volume of data indicates that the probabilities of hydraulic fracturing (fracking) fluids and/or methane contaminating ground water through the hydraulically-created fractures are very low. Since fracking has also raised questions about the economic viability of producing unconventional gas in some parts of the world, supply cost curves are estimated in this paper for the global gas portfolio. The curves show that, in some cases, the costs of producing gas from unconventional reservoirs are comparable to those of conventional gas.
The conclusion is that there is enough natural gas to supply the energy market for nearly 400 years at current rates of consumption and 110 years with a growth rate in production of 2% per year. With appropriate regulation, this may be done safely, commercially, and in a manner that is more benign to the environment as compared with other fossil fuels.
Fractal and power law distributions have been found in the past to be useful for modeling some reservoir properties following the assumptions of constant shape and self-similarity. This study shows, however, that pore throat apertures, fracture apertures, petrophysical and drill cuttings properties of unconventional formations are better matched with a variable shape distribution model (as opposed to constant shape). This permits better reservoir characterization and forecasting of reservoir performance.
Pore throat apertures, fracture apertures, petrophysical properties and drill cutting sizes from tight and shale reservoirs are shown to follow trends that match the variable shape distribution model (VSD) with coefficients of determination (R2) that are generally larger than 0.99. The good fit of the actual data with the VSD allows more rigorous characterization of these properties for use in mathematical models. Data that could not be described previously by a single equation can now be matched uniquely by the VSD. Examples are presented using data from conventional, tight and shale formations found in Canada, the United States, China, Mexico and Australia.
In addition, the study shows that the size of cuttings drilled in vertical and horizontal wells can also be matched with the VSD. This allows the use of drill cuttings, an important direct source of information, for quantitative evaluation of reservoir and rock mechanics properties. The results can be used for improved design of stimulation jobs including multi stage hydraulic fracturing in horizontal wells. This is important as the amount of information collected in horizontal wells drilled through out tight formations, including cores and well logs, is limited in most cases.
It is concluded that the VSD is a valuable tool that has significant potential for applications in conventional, low and ultra-low permeability formations and for evaluating distribution of rock properties at the micro and nano-scale.
Fractal geometry was introduced by Mandelbrot (1982) in his seminal work "The Fractal Geometry of Nature.?? He indicated that this type of geometry applies to many irregular objects in nature. Since then, fractals have been shown to be useful in many disciplines including geology, petroleum engineering, earthquakes, and economics to name a few. In geology, the approach has been used, for example, to evaluate the distribution of natural fractures in outcrops and reservoir rocks; also for evaluating stratigraphic units. In petroleum engineering, they have been used in efforts to capture the distribution of natural fractures for well test analysis. In the case of telluric movements, fractals have been used to evaluate very small to very large earthquakes. In economics, fractals have been used to analyze the distribution of income amongst populations. In the case of the oil and gas industry as a whole, fractal geometry has been used for estimating the spatial distribution of hydrocarbon accumulations (Barton and Scholz, 1995).
The prediction of dynamic elastic constants of reservoir rocks is one of the most important aspects of petroleum engineering. In recent years, several studies have been performed for this purpose. Because of uncertainty and variability in natural materials, deterministic prediction of rock properties in the reservoir is not reasonable. The purpose of this study is to evaluate uncertainty in dynamic-elastic-constant prediction for reservoir rock. Dipole-shear-sonic-image (DSI) log data from one of the Saudi Arabian reservoirs are used to evaluate uncertainty in dynamic-elastic-property prediction. For this purpose, a multiple linear regression (MLR) is carried out to present an empirical equation for shear-wave (S-wave) velocity prediction. Then, probabilistic analysis using Monte Carlo simulation (MCS) is performed to evaluate the uncertainty and reliability in prediction of dynamic elastic constants (Young's modulus and Poisson's ratio). On the basis of the analysis, uncertainty and variability of rock elastic constants are considered, and the value of Young's modulus and Poisson's ratio in a special interval from the reservoir are determined with a certain probability. Finally, the impact of log-data parameters on the value of rock elastic constants in the reservoir interval is assessed.
Probabilistic methods for reserves estimation, including uncertainty quantification and probabilistic aggregation, have gained widespread acceptance in the oil and gas industry, since the first comprehensive guidelines were issued by the Society of Petroleum Engineers (SPE) in 2001. The probabilistic methods now used in the oil industry, as proposed in these guidelines, are similar to those also used in portfolio theory and risk management by the finance industry. A significant amount can be learned from the extensive experience with probabilistic methods and quantification of risk with measures [e.g., value-at-risk (VAR)] in financial risk management. Especially, the guidelines issued by the Basel II Accord (Bank for International Settlements 2006) and the discussions since the 2008 financial crisis contain important lessons.
In this paper, we examine a fundamental question: "Is the P90 reserves value an appropriate measure for quantifying the reserves' downside?" For the P90 reserves value to be considered a good measure of the reserves' downside, it needs to possess a number of basic characteristics involving P90 reserves for each field and the probabilistically aggregated P90 reserves for the portfolio of fields. Analogous to the definition of a coherent risk measure used in the finance industry, we define these characteristics for P90 reserves.
The P90 reserves are as good a risk measure as VAR used in the financial industry. However, like VAR, it is not a coherent risk measure. A possible uncertainty scenario, in which one of these necessary characteristics does not hold, is given. An alternative measure of risk for quantifying the reserves' downside, defined as the average reserves over the confidence interval higher than P90, is presented. This is a coherent risk measure.
In this paper, we highlight the appropriateness and limitations of using the P90 reserves estimate as a measure of the reserves' downside. Understanding of the limitations posed by using the P90 reserves value is vital in management of reserves risk.