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.
Decline curve analysis (DCA) is a graphical procedure used for analyzing declining production rates and forecasting future performance of oil and gas wells. 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. The technique is not necessarily grounded in fundamental theory but is based on empirical observation of production decline.
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. Empirical forecasts can be highly reliable indicators of performance depending on the relevance of the analog data set used to derive the relationships, the quality of the correlations, the quality and reliability of the reservoir data, and the similarity of development conditions between the fields of the analog data set and the reservoir under consideration. There are several empirical relationships in the literature which are often used for quick performance predictions.
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.
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.
It is inherent in the entire system from the reservoir through to the delivery point; and beyond if the product price is included. In order to make valid decisions and plans, the impact of the uncertainty needs to be reflected in a range of possible production outcomes. As highlighted on the topic page Uncertainty analysis in creating production forecasting, many different approaches to handling uncertainty and generating ranges of forecasts are adopted throughout the industry, which can broadly be categorized as'probabilistic' or'deterministic'. This chapter provides some definitions of these terms, explains the documented methodologies that have been used and makes recommendations on best-practice techniques. Uncertainty ranges for many of the inputs to production forecasts, especially in the reservoir, are based on statistically under-sampled datasets and rely on'heuristic' methods to define them.
Whenever we make a production forecast, we use a model of the reservoir and production system, whether it be in the form of a simple mathematical equation (e.g. decline curves) or a full-field 3-D simulation model with surface networking. But no matter how complex the model, it is always a simplification of reality. The aim of the forecaster, in making reliable predictions, is to use a model that is sufficiently representative of the physical processes and constraints, and that adequately allows, on a resource-benefit basis, for treatment of uncertainty in the input and output of the model. Somewhere in the spectrum of modelling techniques, material balance plays an important role; either in supporting a more complex model (e.g. Material balance treats the reservoir as a tank (or limited number of tanks) with uniform properties.