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Abstract When it comes to decline analysis, very little has changed since Arps’ "Analysis of Decline Curve" paper published in 1945. An evaluator tries to visually match a production profile with either an exponential, harmonic or hyperbolic equation. Certainly, French Curves have been replaced by computers that quickly reflect our best guess, but for hyperbolic wells where the profile cannot be represented by a straight line it’s still a guess, granted it’s an educated guess based on experience. This paper will propose that in many instances, automatic forecasting, using an accurate tool, can replace manual forecasting. A common problem faced by all evaluators is how to forecast new wells with little production history. In this paper, an analog forecasting method is proposed that yields excellent results both in predicting budget volumes and matching future production profiles. This method will be demonstrated using back casting studies on real world examples. In SPE 147059, Haskett indicated that the production forecast represented sixty seven percent of the NPV uncertainty in resource play development. Type wells are the primary technique used to generate production forecasts for these new wells. Hence you would expect the oil and gas industry to devote significant research on this topic. When the authors performed a search on SPE One Petro® and Google® for papers about type wells, they only found their two previous papers (SPE 158867 and SPE 162630). In an informal survey of the industry, the authors found that the time slice method of creating type wells was widely accepted. In SPE 158867, Russell et al recommended that the selected well method of creating percentile type wells was preferred over the time slice method. Further research, presented in this paper recommends that the time slice method should be avoided.
Abstract The advent of unconventional petroleum resources has radically altered the landscape when it comes to project development. Most of the traditional methods to predict reserves are no longer useful. This paper will review the traditional evaluation practices and recommend improvements. Forecasting conventional oil and gas reserves has traditionally been done using Arps equations that were designed for Boundary Dominated Flow (BDF), but unconventional wells may experience transient flow for up to a decade or more before the onset of BDF. Arps requires the combination of a super-hyperbolic period that smoothly transitions to an exponential tail to model this unconventional behavior. Unfortunately, the Arps equations offer no information as to when this transition occurs. A new class of empirical decline equations, termed stretched exponentials, is gaining favor in forecasting shale oil or gas. These equations are beneficial because an exponential period is not required. This paper will review the three most popular methods and compare them to Arps. Other decline issues covered include: how much data is necessary to provide a reliable forecast, and how to efficiently forecast hundreds or thousands of wells. When building type wells, it can be challenging to obtain statistically valid and significantly similar wells. For instance, fracture size, fracture fluid type, completion technique, well location and many other factors may need to be considered in order to obtain meaningful groups. This paper will suggest possible groupings and propose methods to confirm their validity. This paper will also demonstrate that type wells created with only historical data, as is standard practice, are logically flawed. When historical production data is merged with reliable production forecasts to build a type well, the resulting type well is the best available representation of the underlying data. A detailed review and analysis of the type well equations plus real-world examples will demonstrate this point.
Abstract Production forecasts provide fundamental input to upstream business decisions, for example in resource volume reporting, field development and production planning. Very often in mature fields, having sufficient cumulative production, field forecasts are performed based on aggregating type curves for new wells and decline curve analysis for existing wells. Conventionally, they are derived by manual trend fitting which is subjective and any iteration incorporating new data is time consuming. The advanced analytics approach presented in this paper can provide rapid and credible forecasts along with more robust quantification of uncertainties as compared to the conventional manual approach. The first step involved in the forecast automation using advanced analytics is data integration in which the production, geological, and surveillance data are combined. Then integrated dashboards are created to infer trends in production behavior. Based on these trends at the most granular forecast level, petroleum engineering based algorithms are developed to automatically select the decline period. Thereafter, multiple scenarios of historical data are generated based on historical allocation and measurement uncertainty. Through each set of these historical scenarios, best fits curves following Arp's equation are extrapolated into the future to generate multiple forecast scenarios. Finally, field level forecast is generated by probabilistically aggregating individual granular level forecasts. The automated forecast program developed is computationally very fast as 500 forecast scenarios at the well level can be generated in 30 seconds, while the full field forecast with 50 wells takes about 30 minutes to generate. Automation of decline period selection and curve fitting eliminates the subjectivity by standardizing the process and reduces the chances of manual errors. Abandonment criteria, discounting and uptime variations can easily be accommodated in the automated process. Visualizations are utilized at each step for quality check and analysis. Forecasts from alternate methodologies are used to validate the forecast ranges coming out from this method. Uncertainties quantification in this approach is found to be more quantifiable and consistent compared to the conventional deterministic approach. Production dashboards created in this workflow by integrating production, surveillance, geological data and forecasts are a very effective tool to perform field reviews and communicate outcomes. The approach described above for decline curve analysis can easily be extended to any type-curve based forecasting. Automation of performance-based decline curve and type curve forecast methodologies has the potential to reduce huge manhours involved in their periodic updates by an order of magnitude that can be utilized to carry out other critical analysis. It is very useful in mature assets with large well inventory, huge dataset and where continuously new data are being added. Standardization of workflow, implementation ease and accuracy will tempt practitioners to use it and thereby develop skills in data analytics.
Introduction Unlike conventional wells which, once drilled sometimes produce for ten or twenty years without significant overhaul, wells in North Dakota often have a short economic run. Producers must constantly drill, fracture and lift to keep production steady. In a climate of very low oil prices where one well cannot quickly pay itself off and finance ＿the ＿next, ＿capital needs to＿-continuously be raised to keep a lease going. The central question addressed in this paper is: what would happen if the money supply dried up and the state and its producers were faced with the prospect of no more capital to drill? How long would current production last? Would some producers or counties fare better than others? And finally, once shut in, what would be the investment necessary to bring production back up? Knowing how production will decline in the face of no new investment should be of paramount importance to anyone passively dependent on its revenues, such as royalty owners, state and civic governments, lien holders and non-operating production partners. Few of these parties have a say in if or how capital is deployed, yet all have an interest in knowing how revenues are likely to drop in a worst case scenario. Instead of confronting the unique production requirements of unconventional wells, people still tend to think and plan in terms of metrics that make sense in a world of assets that depreciate slowly. The usual ways of talking about production are in terms of flowing barrels per day of oil equivalent (boepd) and total reserves. In places like North Dakota, it might be more productive to think of an asset in terms of flowing barrels and its half-life. "Half-life" here is defined exactly as it is in chemistry: the amount of time it will take before boepd is half of the amount it is currently. Equivalently, it is the amount of time over which half of the production in play must be replaced in order to keep overall production steady. This metric can be applied to a single well, a group of wells owned by a particular entity, or an entire county, state or nation. Although analysts often work with decline rates and show graphs of the rise and fall of oil fields, the half-life of a project is very rarely reported.
Abstract Decline curve analysis in unconventional reservoirs is challenging due to the extremely low reservoir flow capacity coupled with the completion techniques and production practices used to achieve economic rates. The primary flow regimes for these wells include transient bilinear flow, transient linear flow, boundary-influenced flow and possibly long-term linear flow. It is commonly believed that the dominant flow regime in unconventional developments will be transient linear flow; however, boundary-influenced flow may be even more important. Classical decline curve analysis techniques are applicable only during the boundary-influenced flow period. Frequently, a hyperbolic form equation is utilized and this practice used by the industry over the entire life of the well results in excessively high decline exponents which can lead to optimistic forecasts of future performance. This technique can be improved for unconventional plays by imposing limits to the final decline. However, current methods to constrain the final decline are based on historical vertical well performance and rules of thumb which introduce a high degree of uncertainty. A number of these plays have now reached a level of maturity that will allow characterization of their long term decline performance. This paper presents a comprehensive technique to analyze production history using a flow regime based workflow to guide classic decline curve analysis. The technique identifies the onset of boundary-influenced flow and thus provides a consistent approach to evaluate late time decline characteristics which will improve the forecast results. In addition to providing guidance for decline curve analysis, the proposed workflow can also be used to evaluate completion efficiency by relating the time to boundary-influenced flow to the stimulated reservoir volume. The practical application of this this technique will be presented using field studies of active unconventional developments.