It follows that forecasts that deviate from the actual production with hindsight can still be good forecasts if the uncertainty range is properly defined, justified and documented as will be shown by Example 6 on the Production forecasting FAQs. However, forecasts that contradict each other even though they are based on the same information cannot be good forecasts, even if they are made for different purposes see Example 3 of the Production forecasting FAQs. It is customary in the industry to describe this uncertainty in terms of a low (P90)/high (P10) range. This is consistent with both the Petroleum Resource Management System (PRMS)  and the Securities and Exchange Commission (SEC) . For volume estimates, a low (P90)/high (P10) range is thus unambiguously defined by statistics.
Uncertainty range in production forecasting gives an introduction to uncertainty analysis in production forecasting, including a PRMS based definition of low, best and high production forecasts. This page topic builds on this with more details of how to approach uncertainty analysis as part of creating production forecasts. Probabilistic subsurface assessments are the norm within the exploration side of the oil and gas industry, both in majors and independents. However, in many companies, the production side is still in transition from single-valued deterministic assessments, sometimes carried out with ad-hoc sensitivity studies, to more-rigorous probabilistic assessments with an auditable trail of assumptions and a statistical underpinning. Reflecting these changes in practices and technology, recently SEC rules for reserves reporting (effective 1 January 2010) were revised, in line with PRMS, to allow for the use of both probabilistic and deterministic methods in addition to allowing reporting of reserves categories other than "proved." This section attempts to present some of the challenges facing probabilistic assessments and present some practical considerations to carry out the assessments effectively. It should be noted that for simplicity the examples referred to in this section are about calculating OOIP rather than generating probabilistic production forecasts directly. Clearly OOIP/GOIP is the starting point of any production forecast and gives a firm basis from which to build production forecasts.
There are two options for the dlim value: "dlimexponential" and "dlimhyperbolic". When using the "dlimexponential", the decline will transition such that the exponential portion of the decline will have an effective decline rate of the dlimvalue specified. When using the "dlimhyperbolic", the decline will transition when the hyperbolic portion reaches the specified dlim value. The exponential portion will then have an effective decline rate that is different from the dlim value. The stretched exponential decline method is a variation of the traditional Arps method, but is better suited to unconventional reservoirs due to its bounded nature.
Oil and gas production rates decline as a function of time; loss of reservoir pressure, or changing relative volumes of the produced fluids, are usually the cause. Fitting a line through the performance history and assuming this same trend will continue in future forms the basis of DCA concept. It is important to note here that in absence of stabilized production trends the technique cannot be expected to give reliable results. There are theoretical equivalent to these decline processes.
Empirical methods are quantitative, statistically-based relationships that allow one to compare performance against a collection of analogous reservoirs using specific reservoir properties. As production forecasting analog methods states, analog methods are generally more qualitative in nature, but it often is possible to derive equations relating reservoir parameters to performance indicators. This should allow narrowing the range of outcomes rather than using the entire range of analog values. There are several empirical relationships in the literature which are often used for quick performance predictions. An example is an equation for recovery efficiency from the API Subcommittee on Recovery Efficiency developed from a statistical study of 80 solution gas-drive reservoirs (API, 1967).
It is often necessary to investigate the forecast uncertainty for a portfolio of fields or reservoirs to evaluate, for example, the risks and opportunities of an exploration portfolio, of a new business strategy, for an "urban planning" study or to evaluate uncertainty in the regional portfolio. It is important to understand whether the portfolio uncertainties are dependent or independent. Cases 1, 2 and 3 are often dependent with complex interactions of the parameters and strong inter-dependencies (both positive and negative correlations) and with common system constraints. In this case, a comprehensive Monte Carlo analysis of the system is recommended that includes all the complex system interactions. Aggregation tools are available in the industry to do this complex probabilistic aggregation after individual field forecasts have been generated; however, most IPSM tools have the ability to evaluate the system uncertainty for all assets concurrently and the latter approach would be preferred, but is sometimes considered too time-consuming.
The motivation for high-performance computing in reservoir simulation has always existed. From the earliest simulation models, computing resources have been severely taxed simply because the level of complexity desired by the engineer almost always exceeded the speed and memory of the hardware. The high-speed vector processors such as the Cray of the late 1970s and early 1980s led to orders-of-magnitude improvement in speed of computation and led to production models of several hundred thousand cells. The relief brought by these models, unfortunately, was short-lived. The desire for increased physics of compositional modeling and the introduction of geostatistically/structurally based geological models led to increases in computational complexity even beyond the large-scale models of the vector processors.
This glossary was created through discussions among the steering committee for the SPE Global Integrated Workshop Series (GIWS) on Production Forecasting. Some definitions were not contested at all, others generated fierce discussions. The contract quantity is the contractually agreed volumes and limits: predefined (annual) volume of natural gas on contract level. A factor applied to forecasts to take into account the fact that a Production System will not always operate at 100% of its capacity. Available But Not Required, that part of the IPSC that is available for production but not produced because of low off-take demand.
Simple analytical interpretation of single well chemical tracer (SWCT) is possible if one assumes uniform oil saturation, negligible hydrolysis during injection and production and assuming similar dispersion for all reservoir layers. In complex reservoir settings, including multilayer test zones, drift, cross-flow etc., reservoir simulation tools, capable of handling the hydrolysis reaction are commonly applied (Jerauld et al., 2010; Skrettingland et al., 2011). In practice, coupled flow and chemical reaction simulators (see e.g. CMG, 2010; and UTCHEM, 2000) are used. Such coupled simulations are CPU-demanding enough that execution time may be an issue, especially when small grid-size are applied to avoid numerical smearing.