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Long term verses short term production forecast

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Many reservoir engineers spend most of their time and effort forecasting for the long-term to meet business objectives where estimates of life-of-field production are required (reserves estimates, valuations of projects etc). However, a number of other business objectives require estimates of production to be made over shorter timescales (nominations under gas contracts, quarterly cash flow estimates and so on). Such a short-term production forecast requires no difference in approach, in principle, to a longer term forecast. However, the important factors affecting the forecast may be different to those affecting a long-term forecast.

Many such factors are related to the performance of production and processing facilities or commercial considerations (such as seasonal variations in gas prices). Forecasting methodologies and workflows have developed to integrate models of these constraints with reservoir models. From a practical point-of-view, this often requires use of reservoir models other than 3D simulation in order for the forecast to be completed on a reasonable timescale. There is, of course, no quantitative definition of “short term” or “long term” that is universally accepted, nor is a distinction between the two always helpful or useful. There is certainly considerable overlap, especially as techniques for creating forecasts are not time-bound in any way.


Short-term

For the purposes these guidelines, short-term forecasts are considered to be of a duration of up to one year, but this is somewhat arbitrary. Note that for the very short-term forecast to answer questions such “what happens when I open this choke?” there is little documented either within companies or the open literature. Probably this is due to the highly operational context in which such questions are usually asked. Furthermore, the short feedback times with respect to the quality of decision making are likely to make the need to document in the literature seem irrelevant to most. Nevertheless, techniques are employed whether they are purely based on experience ie, an extrapolation from what happened the last time a similar operation was performed or more quantitatively with single well models – and often in combination with each other. This may be the extent of the analysis required in order to forecast in a situation where a well’s rate needs to be increased to maintain overall field production.

Methodology and Workflows

Beyond this very short-term requirement, a common workflow to create a forecast (or, indeed, a range of forecasts) is to use a long-term forecast such as that associated with a formal reserves estimate, in which understanding of reservoir dynamics is incorporated, and then to vary this forecast over the short-term period. The variations are typically based on one of the long-term forecasts (for example, the forecast associated with the 2P reserves estimate) and different uptime assumptions made. The resulting forecasts are illustrated schematically in Fig 1.

(a) Uptime Variation on plateau

(b) Uptime variation on “central case” long-term forecast, early to mid-decline

(c) Uptime variation on “central case” long-term forecast, tail end production

Figure 1 Short-term forecasts based on pre-existing long-term forecasts (Pending permission approval)

Note that implicit in this technique is the assumption that variations in production performance arising from reservoir effects are negligible over the duration of the short-term forecast, compared to the variation in uptime. This is probably a reasonable assumption for times shown in Figures 1a and 1b, but towards the end of producing life (Figure 1c), reservoir and uptime effects may be of at least the same order. Another implicit assumption is that the long-term forecast incorporates constraints placed on production by the processing capacities and pressures, or that the variation of these over the short-term forecast is negligible compared to the uptime variation.

Many forecasters use integrated reservoir and surface facilities models to overcome any limitations imposed by the latter constraint. The goal of these methods is to account for the coupling between reservoir, well and facilities constraints where these vary (pressure at various points in the system being the most obvious). Typically, this involves coupling relatively sophisticated models of the surface network and vertical well performance with rather simpler models of the reservoir (certainly compared with 3D reservoir simulation). The main barrier to producing forecasts with fully coupled models is that of model runtime. Frequently, “look up” tables generated from independently run simulation models are incorporated. Typically, forecasts are required to be generated on short timescales and complex coupled models can take several days to run (depending on the size of the problem). Much current research is, therefore, focused on improving runtimes and methodologies so that forecasts can be generated more quickly (see “The State of the Art” below). However, consideration should always be given to the objectives of the forecast and complex, coupled models may not be required. In other words, the significance of any differences between forecasts generated from coupled models or, say, from those generated from existing long-term profiles or look up tables, in which the implicit assumption is that constraints in the total system are independent of each other, is problem dependent.

A review of the literature (Reprint 2010) suggests that the most successful forecasts have the following features:

  • Involve all relevant disciplines in construction of the forecast
  • Data availability for the method applied
  • Supporting documentation states assumptions clearly
  • Explicit inclusion of known events (eg shut ins)
  • Use of models that are as transparent and simple as possible
  • Quantitative assessments of forecast uncertainty
  • Provision of a statement of the economic risk associated with the forecast
  • Checking the forecast against actual performance.

As mentioned earlier, the forecasting methodology used is likely to be problem dependent and chosen based in time available, corporate requirements and other constraints, as much as technical merit. Table 1 indicates some of the important features for the forecaster to consider for each of the broad categories of modelling approach discussed here.


Short-term based on existing long-term forecast
Reservoir/well models included as look up tables
(Partially) Integrated reservoir/well/process models
Involve all disciplines It is likely that a reservoir engineer is preparing the forecast; it is essential that the production engineer who has estimated uptime is at least consulted. Where the forecaster has not prepared the look up tables themselves, consultation with the production or process engineers who have performed these calculations is essential. Integrated models require full understanding by all disciplines involved in their construction. Where models are originally constructed separately by each discipline and then combined, any compromises made in linking them together should be assessed by the relevant discipline.
Data availability The basis of historical uptime, and its calculation method should be documented. The data and its interpretation on which the choice of correlation method for look up tables was made should be communicated and documented. Measured (and any interpolated) data locations should be understood and uncertainty associated with them enumerated.
State assumptions clearly Especially important are: the origin of the base LT forecast (and its underlying assumptions) and the definition of uptime being used. Explicitly document the assumed flow regime in each part of the system for which a correlation curve has been used. Consider and document the implications of steady state versus transient flow assumptions. For an integrated model, it is especially important to state assumptions in a way that is clear to members of other disciplines. Opportunity should be created for each contributor to question others’ assumptions.
Explicit inclusion of known events Where a major “deterministic” event is expected in the forecast period, such an annual shutdown, include this explicitly (eg q=0 bpd) rather than in an average uptime value for the forecast period. Similarly for new well start-ups. Consider the validity and duration of any transient effects associated with major deterministic events. Consider it their inclusion is important for the forecast. Before building in a detailed sequence of operations associated with a deterministic event, consideration should be given to the purpose of the forecast and consequently the level of detail required.
Models transparent and as simple as possible Critically, a succinct summary of the derivation of uptime will be needed. A short, clear summary of the entire model including choice of correlation methods and their underlying assumptions should be possible. The level of detail modelled should be justified and documented in non-specialist terms, so far as possible.
Quantify uncertainties Especially those associated with any implicit assumptions (such as use of a 2P LT forecast as the basis for the ST). Uncertainty in start and/or end dates for deterministic events included should not be forgotten. Likely important uncertainties are those associated with the assumption of steady state flow, and with the choice of correlation method. Any assumptions made to simplify the reservoir, process or well model for ease of computation should have uncertainties quantified, as far as possible. Where limiting assumptions (or compromises) are made, the impact on the forecast should be assessed. Even where a limited effect on production rates is observed, where a wide range in predicted values for a parameter (eg wellhead pressure) is estimated this should be documented.
State economic risks These may not be immediately apparent, but any costs, particularly operating expenditure, should be identified. (Failure to meet the forecast may be as much to lack of budget, as poor methodology) Although not specifically confined to this type of modelling, any economic impact associated with delays to start ups (eg after a shut in for maintenance) or slower return to post event production (“ramp up”) should be estimated. Where uncertainties have been quantified, the economic impact across the range of possible outcomes should be stated. Note that this may not associated just with “lost” production, but with additional cost necessary to achieve this production (e.g. additional pipe insulation).
Check forecast against actual performance In this case, this is largely checking actual uptime with that assumed. However, other factors, such as underlying reservoir performance should not be omitted. Attempt to quantify the source of differences between forecast and actual performance. Where these are associated with the major assumptions of the models (eg choice of correlation, assumption of steady state flow) consider and evaluate possible model adjustments. Even in those instances where forecast production closely estimates actual performance, consider the impact of model assumptions, level of detail etc. Evaluate if it is possible to simplify the models (for ease of communication or other reasons) and retain the same quality of forecast. Additionally, consider the validity of the model for forecasting in the future ie re-assess assumptions, compromises, simplifications and possible changes to future operating model of the wells and facilities.
Table 1 Key features of forecasts for different forecasting methodologies.

 

The state of the art

A large amount has been written on the use of coupled models (sometimes called Integrated Production Models or IPMs) as discussed here; a search of OnePetro, for example, will return more than 17000 references. A detailed review of the literature is beyond the scope of these guidelines and the reader is encouraged to carry out their own research. A good starting point would be the SPE Production Forecasting Reprint (SPE, 2010). More recent examples of the use of IPMs can be found in IPTC-17252-MS and SPE 146968. The former discusses the use of such models for short and long-term forecasting; it highlights the need to critically assess requirements for model constraints with respect to different modelling objectives for different timescales. Crucially, it highlights the need for constant communication between different users of the models to maintain consistency and agree model changes and improvements. SPE 146968 also emphasises the importance of communication between different users of forecasting models. This has been formalised organisationally in this case, at least insofar as model development is concerned. Research into the development of such models focuses on improving run times through evaluation of the degree of coupling required (eg SPE 169243) or by using techniques such as experimental design to optimize the model cases run (eg SPE 166392).

Consistency between short-term and long-term forecasts

In Fig 1, the short-term forecasts assume consistency with the central estimate long-term forecast. Many Operators make such consistency a requirement of their forecasting process. However, in principle, there is no reason why this need be the case, other than for convenience. If forecasts are consistent in this way, it certainly reduces the degree of explanation required to support the forecast and reduces the number of questions asked about its basis. However, relaxing this requirement has some advantages:

  • More up-to-date data (eg recent process performance) can be incorporated
  • Re-working of long-term and short-term forecasts to maintain consistency is reduced

If such an approach is adopted, the practitioner has to be scrupulous in record-keeping (documenting the assumptions and procedures to arrive at the forecast). Rigorous databasing of forecasts also helps in maintaining clarity with respect

References


Noteworthy papers in OnePetro

Amudo, C., Walters, S., O'Reilly, D. I., Clough, M., Beinke, J. P., & Sawiris, R. S. (2011, January 1). Best Practices and Lessons Learned in the Construction and Maintenance of a Complex Gas Asset Integrated Production Model (IPM). Society of Petroleum Engineers. http://dx.doi.org/10.2118/146968-MS.

Kabdenov, S., Aitkazin, M., Macary, S., & Aitzhanov, A. (2014, January 19). IPM Tool for Strategic Decisions: Diverse Applications of IPM in the Supergiant Tengiz Field. International Petroleum Technology Conference. http://dx.doi.org/10.2523/17252-MS.

Tillero, E., Rincón, J., & Nuñez, H. (2014, May 21). An Innovative Workflow for Appropriate Selection of Subsurface-Surface Model Integration Scheme Based on Petroleum Production System Nature, User Needs, and Integrated Simulation Performance. Society of Petroleum Engineers. http://dx.doi.org/10.2118/169243-MS.

Torrado, R. R., Echeverria Ciaurri, D., Mello, U., & Embid, S. (2013, September 30). Fast Reservoir Performance Evaluation Under Uncertainty: Opening New Opportunities. Society of Petroleum Engineers. http://dx.doi.org/10.2118/166392-MS.

Noteworthy books

Society of Petroleum Engineers (U.S.). 2011. Production forecasting. Richardson, Tex: Society of Petroleum Engineers. WorldCat or SPE Bookstore

External links

See also

Category