Conditions for Effective Application of the Decline Curve Analysis Method

Determining the reliable values of the filtration parameters of productive reservoirs is the most important task in monitoring the processes of reserve production. Hydrodynamic studies of wells by the pressure build-up method, as well as a modern method based on production curve analysis (Decline Curve Analysis (DCA)), are some of the effective methods for solving this problem. This paper is devoted to assessing the reliability of these two methods in determining the filtration parameters of terrigenous and carbonaceous productive deposits of oil fields in the Perm Krai. The materials of 150 conditioned and highly informative (obtained using high-precision depth instruments) studies of wells were used to solve this problem, including 100 studies conducted in terrigenous reservoirs (C1v) and 50 carried out in carbonate reservoirs (C2b). To solve the problem, an effective tool was used—multivariate regression analysis. This approach is new and has not been previously used to assess the reliability of determining the filtration parameters of reservoir systems by different research methods. With its use, a series of statistical models with varying degrees of detail was built. A series of multivariate mathematical models of well flow rates using the filtration parameters determined for each of the methods is constructed. The inclusion or non-inclusion of these filtration parameters in the resulting flow rate models allows us to give a reasonable assessment of the possibility of using the pressure build-up method and the DCA method. All the constructed models are characterized by high statistical estimates: in all cases, a high value of the determination coefficient was obtained, and the probability of an error in all cases was significantly less than 5%. As applied to the fields under consideration, it was found that both methods demonstrate stable results in terrigenous reservoirs. The permeability determined by the DCA method and the pressure build-up curve does not control the flow of the fluid in carbonate reservoirs, which proves the complexity of the filtration processes occurring in them. The DCA method is recommended for use to determine the permeability and skin factor in the conditions of terrigenous reservoirs.

Is Decline Curve Analysis the Right Tool for Production Forecasting in Unconventional Reservoirs?

This study presents a novel metaheuristic algorithm that uses a physics-based model for multi-fractured horizontal wells (MFHW) to accurately predict the estimated ultimate recovery (EUR) for unconventional reservoirs. The metaheuristic algorithm creates a sizeable number of stochastic simulations and keeps the simulation results from those random models that closely reproduce observed production data. Unlike other optimization methods, the proposed algorithm does not aim at finding the exact solution to the problem but a group of sufficiently accurate solutions that help to construct the partial solution to the optimization problem as a function of production history. Results from this work provide sufficient evidence as to why traditional decline curve analysis (DCA) is not a suitable solution for production forecasting in unconventional reservoirs. Two case studies are discussed in this work where results from both modeling strategies are compared. Evolutionary prediction of EUR over time using DCA behaves erratically, regardless of the amount of historical production data available to the regression model. Such erratic behavior can, in turn, yield an erroneous estimation of key economic performance indicators of an asset. In contrast, the proposed metaheuristic algorithm delivers precise and accurate results consistently, achieving a significant reduction of uncertainties as more production data becomes available. In conclusion, the proposed partial optimization approach enables the accurate calculation of important metrics for unconventional reservoirs, including production forecasting and expected productive life of an asset.

Hydraulic Fracture Characterization Using Rapid Simulation

The purpose of this paper is to present a technique to estimate hydraulic fracture (HF) length, fracture conductivity, and fracture efficiency using simple and rapid but rigorous reservoir simulation matching of historical production, and where available, pressure. The methodology is particularly appropriate for analysis of horizontal wells with multiple fractures in tight unconventional or unconventional resource plays. In our discussion, we also analyze the differences between the results from decline curve analysis (DCA) approach and the Science Based Forecasting (SBF) results that this work proposes. When we characterize fracture properties with SBF, we can do a better job of forecasting than if we randomly combine fracture properties and reservoir permeability together in a decline-curve trend. The forecasts are significantly different with SBF, therefore fracture characterization plays an important role and SBF uses this characterization to produce different (and better) forecasts.

A Complement to Decline Curve Analysis

Summary Decline curve analysis (DCA) has been the mainstay in unconventional reservoir evaluation. Because of the extremely low matrix permeability, each well is evaluated economically for ultimate recovery as if it were its own reservoir. Classification and normalization of well potential is difficult because of ever-changing stimulation total contact area and a hyperbolic curve fit parameter that is disconnected from any traditional reservoir characterization descriptor. A new discrete fracture model approach allows direct modeling of inflow performance in terms of fracture geometry, drainage volume shape, and matrix permeability. Running such a model with variable geometrical input to match the data in lieu of standard regression techniques allows extraction of a meaningful parameter set for reservoir characterization, an expected outcome from all conventional well testing. Because the entirety of unconventional well operation is in transient mode, the discrete fractured well solution to the diffusivity equation is used to model temporal well performance. The analytical solution to the diffusivity equation for a line source or a 2D fracture operating under constrained bottomhole pressure consists of a sum of terms, each with exponential damping with time. Each of these terms has a relationship with the constant rate, semisteady-state solution for inflow, although the well is not operated with constant rate, nor will this flow regime ever be realized. The new model is compared with known literature models, and sensitivity analyses are presented for variable geometry to illustrate the depiction of different time regimes naturally falling out of the unified diffusivity equation solution for discrete fractures. We demonstrate that apparent hyperbolic character transitioning to exponential decline can be modeled directly with this new methodology without the need to define any crossover point. The mathematical solution to the physical problem captures the rate transient functionality and any and all transitions. Each exponential term in the model is related to the various possible interferences that may develop, each occurring at a different time, thus yielding geometrical information about the drainage pattern or development of fracture interference within the context of ultralow matrix permeability. Previous results analyzed by traditional DCA can be reinterpreted with this model to yield an alternate set of descriptors. The approach can be used to characterize the efficacy of evolving stimulation practices in terms of geometry within the same field and thus contribute to the current type curve analyses subject to binning. It enables the possibility of intermixing of vertical and horizontal well performance information as simply gathering systems of different geometry operating in the same reservoir. The new method will assist in reservoir characterization and evaluation of evolving stimulation technologies in the same field and allow classification of new type curves.

Rescaling Method for Improved Machine-Learning Decline Curve Analysis for Unconventional Reservoirs

Summary Decline curve analysis (DCA) has been widely applied in production forecasting of wells in unconventional hydrocarbon reservoirs. However, traditional curve-fit-based methods fall short of forecast accuracy due to three weaknesses: first, they cannot capture the reservoir signals not modeled by the underlying DCA model formulas; second, when predicting the production of a target well, the production history of other wells in the geologic formation (which is valuable information) is not considered; third, the wells’ geographic, geologic, wellbore, well spacing, and completion properties, which are highly relevant to production capability, are not used. More recent approaches have begun replacing traditional DCA with machine-learning methods [e.g., random forest (RF), support vector regression (SVR), etc.] for production forecast. Nevertheless, these methods are still suboptimal in detecting similar production trends in different wells, leading to large forecast error. A new and simple method called dynamic production rescaling (DPR) is developed to improve the accuracy of machine-learning DCA (ML-DCA). By combining DPR with common ML-DCA methods, we observe that the error mean, deviation, and skewness can be significantly reduced by 15 to 35% compared with ML-DCA without DPR. The error reduction is 30 to 60% compared with automatic curve fit of the traditional modified Arps DCA model. DPR has been tested successfully on monthly production data of over 20,000 unconventional horizontal wells in the Permian and Appalachian basins for both long- and short-term forecasts. The significant error reduction is consistent across different basins and formations. DPR is computationally efficient, so a large number of wells can be analyzed automatically and quickly. Moreover, the effectiveness and efficiency of DPR is independent of the underlying machine-learning algorithm, further demonstrating its robustness.