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Abstract The prediction of Estimated Ultimate Recovery (EUR) for a well or group of wells in a development project is critical to accurate reserves estimation. A number of techniques, many of which can be used deterministically or probabilistically, are employed in EUR prediction in mature and maturing unconventional gas and oil plays in North America. These include the use of geological and production data from analogous reservoirs, the use of volumetric methods and recovery factors, analytical models, numerical reservoir simulation and production decline curve analysis (DCA). Decline curve analysis is arguably the most commonly used method for forecasting reserves in unconventional reservoirs. This paper discusses its basic theory and application, together with the potential pitfalls of using simple empirical production forecasting methods in complex reservoirs. We analyse production data from several US unconventional oil and gas plays and carry out production forecasting using the traditional Arps' methods as a basis for comparison, and newer empirical solutions including the Power Law, Stretched Exponential Decline Model, Duong (and variations thereof). The range of production forecasts provided by these methods is examined, together with methodologies for developing statistically valid type wells in unconventional plays, and how best to determine valid input parameters for the various empirical solutions. The effect of the variable length of production history available in the various plays, and how it impacts the accuracy of the forecasts is also examined. The results of the analyses are compared with analytical models developed for each play to determine the suitability of each decline curve analysis method: in which plays and under which circumstances they can be applied, and suggest reasonable input parameters and data requirements for each method. Finally, the potential future use of the methods in emerging plays outside of North America is presented.
Abstract With the emergence of liquid rich shale (LRS) plays like Eagle Ford and Northern Barnett, the petroleum industry needs a simple, easily applied technique that provides reliable estimates of future production rates in this kind of reservoir. There is no guarantee that methodology that has proved to work in gas reservoirs will necessarily be appropriate in LRS reservoirs. In this work, we found that without corrections of early data, the Stretched Exponential Production Decline (SEPD) model, designed for transient flow, usually produces pessimistic forecasts of future production. The Duong method, another transient model, may be reasonable during long term transient linear flow, but notably optimistic after boundary-dominated flow (BDF) appears. For wells in BDF, the Arps model provides reasonable forecasts, but the Arps model may not be accurate when applied to transient data. A hybrid of early transient and later BDF models proves to be a reasonable solution to the forecasting problem in LRS. In addition, use of diagnostic plots (like log-log rate-time and log-log rate-material balance time plots) improves confidence in flow regime identification and production forecasting. In some LRS's, BDF is observed within 12 months. In any case, it is essential to identify or to estimate the time to reach BDF and to discontinue use of transient flow models after BDF appears or is expected. We validated our methodology using "hindcast analysis"; that is, matching the first half of production history to determine model parameters, then forecasting the second half of history and comparing to observed production data. We also found that application of pressure-corrected rates in decline curve analysis (DCA) may substantially improve the interpretation of data from unconventional oil wells flowing under unstable operating conditions. Fetkovich (hydraulically fractured well) type curve analysis can be added to improve confidence in flow regime identification from diagnostic plots and to estimate the Arps hyperbolic exponent b from the matching b stem on the type curve, which can then be extrapolated to determine estimated ultimate recovery.
Abstract When it comes to forecasting production from shale plays that are subject to multistage hydraulic fracturing, most modeling approaches do not apply throughout the life of a well. The Duong decline method, introduced in 2010, is no exception. When a well reaches the stage of boundary-dominated flow (BDF), the method's limitations become clear. Part 2 of the Duong method proposes to extend the approach in order to apply it to the long-term performance of wells that are influenced by various fracture fabrics, well spacing, and fluid types such as gas and saturated and unsaturated oil production. In addition to overcoming its own limitations, the extended method is also intended to rectify limitations associated with other commonly used production forecasting methods. The outcome of this work should generate a model that accounts for the physical processes of flow regimes in horizontal wells with multistage hydraulic fracturing. This extension employs empirical, analytical, and numerical solutions to represent a depletion model that consists of multiple realistic flow regimes. The method uses the Duong diagnostic plot, log(q/Gp) versus log(t), to normalize the constant rate and constant pressure analytical solutions during both linear flow and BDF. This forms an equivalent Fetkovich-type curve for unconventionals and serves as the base curve for identifying the start time of fracture interference among the fractures and in connection with the Arps' b values. Results from numerical simulation modeling are used to fill in long-term production estimates affected by various fracture geometries, well drilling spacing units, and fluid types. Type-curve parameters include start fracture interference time and fluid influx ratio for each depletion system. The fluid influx ratio based on permeabilities, fracture distance and half-length, and well spacing ranges from zero to one, where zero represents an isolated system and one represents transient conditions. The outcome of this work should help the industry not only to forecast rate production more accurately, but to better understand decline prediction in tight oil and shale gas reservoirs. The paper also discusses methods to estimate input parameters for forecasting, using factors such as permeabilities, fracture interference time, stimulated-rock volume (SRV), and fracture half-length from production history and completion data. The paper applies field and simulation data to demonstrate the use of the new extension.
This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 167734, “Comparison of Empirical and Analytical Methods for Production Forecasting in Unconventional Reservoirs: Lessons From North America,” by Riteja Dutta, SPE, Marie Meyet, SPE, and Chris Burns, SPE, Baker Hughes, and Frederik Van Cauter, SPE, OMV, prepared for the 2014 SPE/EAGE European Unconventional Resources Conference and Exhibition, Vienna, Austria, 25–27 February. The paper has not been peer reviewed.
Decline-curve analysis (DCA) is arguably the most commonly used method for forecasting reserves in unconventional reservoirs. This paper discusses its basic theory and application, together with the potential pitfalls of using simple empirical production-forecasting methods in complex reservoirs. Production data from several US unconventional plays are analyzed, and production forecasting is carried out with the traditional Arps methods as a basis for comparison. The results are compared with analytical models developed for each play to determine the suitability of each DCA method.
At the most fundamental level, DCA involves fitting an empirical model of the trend in production decline from a well’s history and projecting the trend into the future to determine the well’s economic life and forecast cumulative production.
Arps Curves. The Arps decline model is established from the empirical observation that the loss ratio (the rate of change of the reciprocal of the instantaneous decline rate) is constant with time.
Power-Law Exponential (PLE) Method. The PLE relation draws on the classical Arps decline curves but uses a different description of the nominal decline-rate parameter (D). The PLE method assumes the loss ratio (1/D) is approximated by a decaying power-law function with a constant behavior at early time.
Duong Method. This rate-decline approach was developed specifically for fractured shale-gas reservoirs. It assumes that the connected fracture density of the reservoir increases over time because of stress changes with pressure depletion in a linear-flow regime.
Stretched-Exponential Production Decline (SEPD). SEPD can be described as the integral of all exponential decays, with a fat-tailed probability distribution of the time constants. The model attempts to mimic heterogeneity by describing the production-decline behavior of the reservoir as a group of exponential declines from a number of contributing volumes, with a specific distribution of time constraints.
Analytical-Model Theory Analytical models are mathematical models that have a closed-form solution (i.e., the solution to the equations used to describe changes in a system can be expressed as a mathematical analytic function). Analytical models can be used in their simplest form to validate decline projections made with DCA methods.
Developing the Analytical Model. Analytical models for each play were developed to attempt production forecasting beyond the available history for each type well. DCA methods and parameters are validated by comparing the results of the analytical model, assuming a good history match is achieved, with the production data from the type wells.
Decline-curve analysis (DCA) is arguably the most commonly used method for forecasting reserves in unconventional reservoirs. Production data from several US unconventional plays are analyzed, and production forecasting is carried out with the traditional Arps methods as a basis for comparison. The results are compared with analytical models developed for each play to determine the suitability of each DCA method. At the most fundamental level, DCA involves fitting an empirical model of the trend in production decline from a well's history and projecting the trend into the future to determine the well's economic life and forecast cumulative production. The Arps decline model is established from the empirical observation that the loss ratio (the rate of change of the reciprocal of the instantaneous decline rate) is constant with time.