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The objective of this paper is to present a comprehensive methodology to perform an initial evaluation of market size and potential value of a new hydrocarbon's transportation and commercialization network in Mexico. The methodology is based in an integrated multi-domain evaluation from geoscience and reservoir engineering to facilities design and economics; covering four main aspects: 1) Confirmation of reserves and estimated resources, 2)Generation of production forecasts, 3) Pipeline network design for two sales terminals and 4) design plot of sales terminals with processing facilities to handle expected production. The four aspects are supported by an economic evaluation with multiple tariff scenarios for varied break-even and profitability targets. The initial confirmation of reserves and resources for more than 70 fields spanning greenfields and brownfields alongside with exploration areas yielded the basis for the analysis which was supported on statistical analysis of analogue fields combined with a geological interpretation of the target basins. Production forecasts from multiple scenarios grouped by region and nationwide confirmed expected production input into the newly proposed transportation network which was compared against forecast from some of the operators with a reasonable match. Pipeline network design for heavy and light crude with flow assurance considerations provided the pipelines diameters and length to consider for the economic case and the inlet pressure for two terminals. Finally, the design plot of the facilities required at the terminals allowed to size land and infrastructure requirements at a pre-Front End Engineering Design (FEED) level. The economics showed feasibility of the production input scenarios at different tariff for the trading company considering the initial investments required to build the new transportation and commercialization network. A novel integrated engineering solution combining domain expertise with processing and facilities design covering reserves and resources confirmation for more than 70 fields, production forecasts for all fields and aggregated to regional level, general assessment on infrastructure required to build a new transportation network and finally the infrastructure pre-design for two processing and commercialization terminals.
Recently, flowback data analysis enabled us to evaluate important fracture parameters including fracture conductivity and volume in unconventional reservoirs. To perform the analysis, diagnostic plots, straight-line techniques, and history-matching techniques have been used. Immediate water and gas production usually occurs on flowback in shale gas wells. In this paper, a novel workflow is developed for the analysis of water flowback data and early-time production of shale gas wells. This analysis then helps to define the movable water and the applicability of the soaking process on the shale gas well.
Rate transient analysis (RTA) combined with decline curve analysis (DCA) was used to analyze different shale gas wells. Effective fracture volume and geometry were calculated from the RTA analysis. Estimated ultimate water recovery was calculated from DCA. The calculated water-in-place and the estimated ultimate water recover (EURw) will be compared against the injected fracturing fluid.
Water RTA result show that in the case of shale wells with no movable formation water, gas kick off early, and boundary dominated flow (BDF) was observed. In addition, these wells performance improved with soaking process. On the other hand, if initial formation water saturation is higher than the connate water, water production will be from the frac fluid and formation water. As a result, gas kick off delays and transient flow regimes are expected. Soaking process can have a negative impact on the well performance if the movable water saturation is high.
Honoring the flowback data can help to estimate the fracture geometry and to judge the quality of the shale formation quality and its validity for soaking process.
Production forecasting in unconventional reservoir systems is an important problem for the industry. While "averaging" observed production profiles is often used to construct type wells, model-based methods have significant advantages, including observance of basic fluid-flow principles. Model-based approaches for type well construction involve using analytical and numerical models for a representative well in a group of wells. Scaling factors are used to apply the model to the rest of the wells in the group. However, there is uncertainty in the estimates of these scaling factors that is not accounted for when we use point estimates. In this work, we calculate the uncertainty in the scaling factors in a group of analog wells. We then account for this uncertainty in the estimation of the P10, P50 and P90 type wells within the group of analog wells, using a Bayesian hierarchical model.
We select a group of analog wells and fit a model to a representative well in the group. We then calculate the scaling factor for each well in the group as a probability distribution. We pool these distributions to estimate the variation in scaling factors between different wells. This represents the uncertainty in the scaling factors across the group. We translate this into uncertainty in the production rate profile across the group. Finally, we generate P10, P50 and P90 production profiles from the distribution. These represent type wells for the group of analog wells.
We show that our technique is superior to point estimates of scaling factors traditionally used in the industry, due to pooling of distributions. Point estimates ignore possible variation in the scaling factor due to noise and presence of outliers. We show that this could accumulate and have a significant impact on the estimated type well. This propagates to the calculation of estimated ultimate recovery. We validate our method with simulated data, with and without added noise.
Forecasting production for producing wells and estimating production on proved undeveloped reserves are problems of engineering and economic importance. Existing methods using empirical averaging of production profiles or point estimates for scaling factors fail to use information on the uncertainty of the scaling factors. By discarding this information, we discard uncertainty that could influence reserves estimates. We present a method that incorporates this uncertainty into the analysis transparently. Our method can also be used to pool uncertainty across various well groups.
According to the 2015 Energy Information Administration (EIA) global assessment (
The Vaca Muerta formation was subdivided into sub-areas based on the fluid type. The most appropriate production decline model was determined for each sub-area and coupled with Markov-Chain-Monte-Carlo (MCMC) methodology to analyze and forecast production of existing wells to calculate reserves. The analyses of individual wells in each sub-area were used to create probabilistic type-decline curves. These curves were combined with the estimated acreage-per-well distributions and the remaining drillable areas per sub-area to estimate contingent and prospective resources. The difference between contingent and prospective resources was based on the distance from existing wells.
As of January 2018, the total reserves (P90–P50–P10) for the Vaca Muerta shale in Argentina associated with existing wells are estimated to be 8.5–17.5–38.4 MMm3 of oil and 9.5–27.2–74.6 Bm3 of gas. Estimated contingent resources are 8.8–50.6–181.1 MMm3 of oil and 2.6–16.4–51.5 Bm3 of gas. Estimated prospective resources are 424–2,464–8,771 MMm3 of oil and 211–1,279–3,483 Bm3 of gas. Resources and reserves estimates were combined to estimate technically recoverable resources (TRR). The estimated TRR are 443–2533–8992 MMm3 of oil and 223–1223–3609 Bm3 of gas.
The results of this work should provide a more reliable assessment of the reserves and resources in the Argentine Vaca Muerta shale than previous estimates based on volumetric methodologies and analogies.
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) are not considered; third, the wells' geographic, geologic, wellbore, well spacing, and completion properties, which are highly relevant to production capability, are not utilized. More recent approaches have begun replacing traditional DCA with machine-learning methods (e.g., Random Forest, Support Vector Regression, etc.) for production forecast. Nevertheless, these methods are still sub-optimal in detecting similar production trends in different wells, leading to large forecast error.
A simple and novel 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 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 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.
Operation and management of unconventional hydrocarbon reservoirs requires production forecast of each well. This enables better development planning, economic outlook, reserve estimates, and business decisions such as trading and pricing strategies. A methodology called Decline Curve Analysis (DCA) has been widely applied in production forecast of wells in unconventional reservoirs, and many analytical DCA models are available for use. They describe the production physics in analytical equations of flow rate versus time, and the coefficients of the equations are computed from curve-fitting the production history.
Decline curve analysis has been the mainstay in unconventional reservoir evaluation. Due to 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 due to ever changing stimulation practices. The standard methodology for conducting decline curves gives us parameters associated with 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 modelling of inflow performance in terms of fracture geometry, drainage volume shape, and matrix permeability. Running such a model with variable geometrical input to match data in lieu of standard regression techniques allows extraction of a meaningful parameter set for reservoir characterization.
Since the entirety of unconventional well operation is in transient mode, the discrete 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, semi-steady state solution for inflow, although the well is neither 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.
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. Prior results analyzed by traditional decline curve analysis 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.
The new method will assist in reservoir characterization, evaluation of evolving stimulation technologies in the same field, and allow classification of new type curves.
The objective of the current study is to integrate dynamic well performance data with static reservoir volumetric constraints to obtain an improved characterization of the transient drainage volume and predicted decline of a well. The specific application is to unconventional reservoirs where the time to reach boundary dominated flow is large compared to the period of production and so classical decline curve analysis is not directly applicable.
We have previously introduced the concept of the diffusive time of flight and the transient drainage volume of a well for infinite acting systems. It has been applied to field performance analysis and, for instance, used to rank well re-fracturing candidates. These same concepts may be extended from production analysis to decline prediction for a bounded reservoir through the definition of a transient well productivity. The result is a first order ordinary differential equation that may be integrated numerically to predict future production decline. This approach allows us to integrate static reservoir volume constraints (treated as an uncertainty) to obtain a probabilistic decline curve and probabilistic EUR estimates, conditioned to the well performance data. Implementation has required the development of improved field data manipulation techniques compared to our earlier studies. The steps of outlier identification, noise reduction, and regularization of the inversion for the underlying drainage volume geometry are also included in this study.
The methods are tested on approximately two dozen Eagle Ford wells, providing data driven determination of their transient drainage volumes and decline curve predictions. The decline curves are similar in character to a stretched exponential or Duong’s decline curve for early time. However, the new results differ in mid to late time as the use of a static reservoir volume constraint removes the need for an arbitrary cutoff when calculating the ultimate recovery and/or the EUR.
A novel methodology has been developed based upon the diffusive time of flight, which extends the earlier infinite acting analysis for the transient drainage volume to the transient well productivity for a bounded reservoir. This has allowed us to obtain a new class of decline curves that are driven by well performance data, while providing finite production predictions at late time, removing the need for arbitrary cutoffs.
The methodology provides a means of integrating static reservoir volumes (a geologic constraint) with well production data so that the predicted decline is not purely extrapolated from the well data but is instead a transient interpolation that honors both the production and static data.
Production forecasting of wells in unconventional reservoirs is an important problem for the industry. Type well construction is a commonly used procedure to forecast aggregated wells. However, "averaging" observed production profiles, a commonly used type well construction method, is strongly affected by the presence of wells with noisy production history. Forecasting individual wells on the other hand is time consuming, and inaccurate for wells with limited production history. In this work, we propose model-based type wells for forecasting production for individual wells, using scaling factors.
We cluster wells based on their observed flow regimes. We then fit a model to a representative well in each cluster. This model could be analytical or numerical, depending on the data available. We then use Bayesian hierarchical modeling to scale the model to each well. We also obtain the distribution of the mean scaling factor for the wells in the cluster. This accounts for the uncertainties in the model fit, the individual well scaling, and the spread of wells within the cluster. Finally, we obtain the P10, P50 and P90 production profiles and forecasts for the wells from the respective scaling factors.
We show that our technique has lower uncertainty and better prediction for noisy wells and wells with limited production data, when compared to individually fitted models. This is because our technique uses partial pooling to "borrow" information from wells with longer production histories. We demonstrate that our technique is robust to the inclusion of noisy wells in the cluster. We demonstrate the effect of the noise and the number of wells in the cluster, on the uncertainty of the forecast. We validate this technique with simulated data, with and without added noise.
An important goal in production forecasting is to avoid systematic overestimation of reserves. This goal can be achieved when the uncertainties in the estimating process are accounted for in a principled manner. In this work, we account for model, well and cluster uncertainties in the process of type well construction. At late times, when the number of producing wells to be averaged is lower, the widely used method of averaging type wells fails. Our technique is not affected by this problem, since we consider the entire production history of a well when calculating the shift factors. Using our method, we calculate production forecasts that are well calibrated to any required cumulative probability.
This course entails estimating reserves in diverse reservoir settings. The participants will gain insights into several established methods, such as material-balance analysis (MBA), decline-curve analysis (DCA), and rate-transient analysis (RTA) for estimating the in-place volume associated with a given well in actual field settings, wherein various drive mechanisms may be in play. The discussion will also entail regulatory guidelines of SEC and the general guidance offered by SPE-sponsored Petroleum Resources Management System (PRMS). This half-day course emphasizes understanding of various analytical tools for understanding flood performance, leading to the assessment of remaining reserves. Specifically, we use the water-oil/watergas ratio type curve and the reciprocal-PI plot for monitoring flood performance at individual producers.