US unconventional resource production has developed tremendously in the past decade. Currently, the unconventional operators are trying many strategies such as refracturing, infill drillings and well spacing optimization to improve recovery factor of primary production. They are also employing big data and machine learning to explore the existed production data and geology information to screen the sweet spot from geology point of view. However, current recovery factor of most unconventional reservoirs is still very low (4~10%). A quick production rate decline pushes US operator to pursue gas EOR for unconventional reservoirs, lifting the ultimate recovery factor to another higher level. The goal of this work is to improve oil recovery by implementing gas Huff and Puff process and optimizing injection pattern for one of the US major tight oil reservoirs - Eagle Ford basin. Gas diffusion is regarded as critical for gas Huff and Puff process of tight oil reservoirs. Utilizing the dual permeability model, gas diffusion effect is systematically analyzed and compared with the widely used single porosity model to justify its importance. Transport in natural fractures is proved to be dominated recovery mechanism using dual permeability model. Uncertainty studies about reservoir heterogeneity and nature fracture permeability are performed to understand their influences on well productivity and gas EOR effectiveness. Moreover, three alternative gas injectant compositions including rich gas, lean gas and nitrogen are investigated in gas Huff and Puff processes for Eagle Ford tight oil fractured reservoir. The brief economic evaluation of Huff and Puff project is conducted for black oil region of the Eagle Ford basin.
Hong, Aojie (National IOR Centre of Norway and University of Stavanger) | Bratvold, Reidar B. (National IOR Centre of Norway and University of Stavanger) | Lake, Larry W. (University of Texas at Austin) | Ruiz Maraggi, Leopoldo M. (University of Texas at Austin)
Aojie Hong and Reidar B. Bratvold, National IOR Centre of Norway and University of Stavanger, and Larry W. Lake and Leopoldo M. Ruiz Maraggi, University of Texas at Austin Summary Decline-curve analysis (DCA) for unconventional plays requires a model that can capture the characteristics of different flow regimes. Thus, various models have been proposed. Traditionally, in probabilistic DCA, an analyst chooses a single model that is believed to best fit the data. However, several models might fit the data almost equally well, and the one that best fits the data might not best represent the flow characteristics. Therefore, uncertainty remains regarding which is the "best" model. This work aims to integrate model uncertainty in probabilistic DCA for unconventional plays. Instead of identifying a single "best" model, we propose to regard any model as potentially good, with goodness characterized by a probability. The probability of a model being good is interpreted as a measure of the relative truthfulness of this model compared with the other models. This probability is subsequently used to weight the model forecast. Bayes' law is used to assess the model probabilities for given data. Multiple samples of the model-parameter values are obtained using maximum likelihood estimation (MLE) with Monte Carlo simulation. Thus, the unique probabilistic forecasts of each individual model are aggregated into a single probabilistic forecast, which incorporates model uncertainty along with the intrinsic uncertainty (i.e., the measurement errors) in the given data. We demonstrate and conclude that using the proposed approach can mitigate over/underestimates resulting from using a single decline-curve model for forecasting. The proposed approach performs well in propagating model uncertainty to uncertainty in production forecasting; that is, we determine a forecast that represents uncertainty given multiple possible models conditioned to the data. The field data show that no one model is the most probable to be good for all wells. The novelties of this work are that probability is used to describe the goodness of a model; a Bayesian approach is used to integrate the model uncertainty in probabilistic DCA; the approach is applied to actual field data to identify the most-probable model given the data; and we demonstrate the value of using this approach to consider multiple models in probabilistic DCA for unconventional plays. Introduction Although numerical techniques for forecasting hydrocarbon production have developed rapidly over the past decades, DCA remains an industry-accepted method and is used extensively in the oil and gas industry. Decline-curve models are very computationally attractive because only production data, which can be easily acquired, are required for determining a few parameter values through history matching.
Field data have shown the decline of fracture conductivity during reservoir depletion. In addition, refracturing and infill drilling have recently gained much attention as efficient methods to enhance recovery in shale reservoirs. However, current approaches present difficulties in efficiently and accurately simulating such processes, especially for large-scale cases with complex hydraulic and natural fractures.
In this study, a general numerical method compatible with existing simulators is developed to model dynamic behaviors of complex fractures. The method is an extension of an embedded discrete-fracture model (EDFM). With a new set of EDFM formulations, the nonneighboring connections (NNCs) in the EDFM are treated as regular connections in traditional simulators, and the NNC transmissibility factors are linked with gridblock permeabilities. Hence, manipulating block permeabilities in simulators can conveniently control the fluid flow through fractures. Complex dynamic behaviors of hydraulic fractures and natural fractures can be investigated using this method.
The proposed methodology is implemented in a commercial reservoir simulator in a nonintrusive manner. We first present one synthetic case study in a shale-oil reservoir to verify the model accuracy and then combine the new model with field data to demonstrate its field applicability. Subsequently, four field-scale case studies with complex fractures in two and three dimensions are presented to illustrate the applicability of the method. These studies involve vertical- and horizontal-well refracturing in tight reservoirs, infill drilling, and fracture activation in a naturally fractured reservoir. The proposed approach is combined with empirical correlations and geomechanical criteria to model stress-dependent fracture conductivity and natural-fracture activation. It also shows convenience in dynamically adding new fractures or extending existing fractures during simulation. Results of these studies further confirm the significance of dynamic fracture behaviors and fracture complexity in the analysis and optimization of well performance.
Controlled laboratory experiments and some field studies have shown that the onset of sand production in gas wells differs from that in oil wells. Results from a general 3D sand-production numerical model are presented to explain the differences in the onset of sanding and sand-production volume for different fluids and under different flow and in-situ stress conditions. The sand-production model accounts for multiphase-fluid flow and is fully coupled with an elasto-plastic geomechanical model. The sanding criterion considers both mechanical failure and sand erosion by fluid flow. Non-Darcy flow is implemented to account for the high flow rates. The drag forces on the sand grains are computed on the basis of the in-situ Reynolds number. Both the intact rock strength and the residual rock strength depend on water saturation. Water evaporation (drying) resulting from gas flow is modeled using phase equilibrium calculations.
The onset of sand production is compared for different fluid types (oil and gas). Model results are shown to be consistent with experimental observations reported in the literature. For example, the onset of sanding is observed at higher compressive stresses for gas wells as compared with oil wells. The primary mechanism for this is for the first time shown to be sand strengthening induced by evaporation of water. This effect is not observed in oil wells. The sand-production rate when non-Darcy effects are considered is lower than for Darcy flow. The reason for this is the lower fluid velocity (for the same drawdown) and, consequently, smaller drag forces on the failed sand grains. The effect of water breakthrough and water cut on sand production is studied from both mechanical and erosion perspectives. The model is shown to be capable of accurately predicting the onset of sanding and sand production induced by multiphase- and compressible-fluid flows, helping us to predict sanding issues in both oil and gas wells.
Previous experimental observations have shown the formation of distinct failure patterns and cavity shapes under different stress and flow conditions. With isotropic stress, spiral failure patterns with localized shear bands are likely to form. On the other hand, under anisotropic stress, V-shaped cavities, dog-ear cavities, or slit-mode cavities are usually observed. However, the mechanisms for the development of these sanding cavities have not been fully articulated. In addition, to accurately predict the onset of sanding and to predict the sand-production rate, it is crucial to capture the physics of the formation of these cavities during sand production.
This paper presents a fully coupled poro-elasto-plastic, 3D sand-production model for sand-production prediction around openhole and perforated wellbores in a weakly consolidated formation. Sanding criteria are based on a combination of shear failure, tensile failure, and compressive failure from the Mohr-Coulomb theory and strain-hardening/softening. After the failure criteria are met, an algorithm for the entrainment of the sand based on the calculation of hydrodynamic forces is implemented to predict sand erosion and transport. Dynamic mesh refinement has been implemented to effectively capture the strain-localization regions.
The model has been validated with multiple analytical solutions. In addition, it is applied to compare with previous sand-production experiments that have explored the different cavity shapes formed under different conditions. The model is capable of not only explaining the mechanisms responsible for each type of cavity shape but also predicting the cavity shape that will be formed under a specific set of conditions. Parametric studies for these cases provide an additional insight into the important role that the post-yield, poro-elastoplastic properties of the sand play in controlling the sanding mechanisms and cavity development. This allows us to predict, much more accurately, the onset of sanding and the sanding rate.
Leung, Wing T. (University of Texas at Austin) | Chung, Eric T. (The Chinese University of Hong Kong) | Efendiev, Yalchin (Texas A&M University) | Vasilyeva, Maria (Texas A&M University) | Wheeler, Mary (University of Texas at Austin)
The objective of this work is to design upscaled model concepts for multi-phase flow and transport. Our approaches are based on recent developments in multiscale simulations and their relations to upscaling.
We propose a novel multi-phase upscaling technique, which employs rigorous multiscale concepts based on the Constraint Energy Minimization (CEM-GMsFEM). CEM-GMsFEM concepts utilize local spectral problems and an energy minimization principle to design multiscale basis functions, which are supported in oversampled regions. A coarse-grid solution defined by these basis functions provides first-order accuracy with respect to the coarse-mesh size and is independent of high contrast of the permeability. The degrees of freedom in multiscale methods represent the coordinates of the solution in the multiscale space. To design an upscaled model, we modify these basis functions such that the degrees of freedom have physical meanings, in particular, the averages of the solution in each continuum. This allows deriving rigorous upscaled models and account for both local and non-local on the effects. The transmissibilities in our upscaled models are non-local and take into account non-neighboring connections.
To extend this approach to nonlinear problems in the context of two-phase flow, we develop non-linear upscaling, where the pressures and saturations are interpolated within an oversampled region based on average values of these quantities. Multicontinua concepts are used to localize the problem to the oversampled regions. Our upscaled model shares some similarities with the pseudo-relative permeability approach with the following differences: (1) the upscaled relative permeabilities depend non-locally on the saturations; and (2) local problems, formulated in oversampled regions, involve constraints and require multi-contiuum concepts.
The numerical results will utilize upscaled methods to predict the solution of single-and two-phase flow dynamics. We will describe upscaled equations, which include the non-local neighborhood connections. Our results demonstrate that the proposed approaches provide a good accuracy and robustness. We consider various types of heterogeneities. The proposed concepts will benefit developing coarse-grid and upscaled models for many applications involving multi-phase flow and transport.
Acidizing in un-fractured carbonate reservoirs has been well studied through modeling and simulation. Since carbonate reservoirs are often naturally fractured, fractures should be modeled for realistic acidizing operations. We present adaptive enriched Galerkin (EG) methods to simulate acidizing in fractured carbonate reservoirs. We adopt a two-scale continuum model for the acid transport. The coupled flow and reactive transport systems are spatially discretized by EG methods. Fractures are introduced using local grid refinement (LGR) technique. Adaptive mesh refinement (AMR) is implemented around wormhole interfaces. Simulation results show that acidizing in fractured carbonate reservoirs is largely dependent on the fracture system while acidizing in unfractured carbonate reservoirs is mainly determined by operation parameters such as acid injection rate. Computationally, the proposed EG scheme has less numerical dispersion and grid orientation effects than standard cell center finite difference/volume methods. AMR is very efficient to track the wormhole growth and speed up acidizing simulations.
Hassan, Ahmed (King Abdullah University of Science and Technology) | Chandra, Visawanthi (King Abdullah University of Science and Technology) | Yutkin, Maxim P. (King Abdullah University of Science and Technology) | Patzek, Tadeusz W. (King Abdullah University of Science and Technology) | Espinoza, D. N. (University of Texas at Austin)
Microporous carbonates contain perhaps 50% of the oil left behind in current projects in the giant carbonate fields in the Middle East and elsewhere. Pore geometry, connectivity, and wettability of the micropore systems in these carbonates are of paramount importance in finding new improved-oil-recovery methods. In this study, we present a robust pore-imaging approach that uses confocal laser scanning microscopy (CLSM) to obtain high-resolution 3D images of etched epoxy pore casts of the highly heterogeneous carbonates. In our approach, we have increased the depth of investigation for carbonates 20-fold, from 10 µm reported by Fredrich (1999) and Shah et al. (2013) to 200 µm. In addition, high-resolution 2D images from scanning electron microscopy (SEM) have been correlated with the 3D models from CLSM to develop a multiscale imaging approach that covers a range of scales, from millimeters in three dimensions to micrometers in two dimensions. The developed approach was implemented to identify various pore types [e.g., intercrystalline microporosity (IM), intragranular microporosity (IGM), and interboundary sheet pores (SPs)] in limestone and dolomite samples.
The operational use of nanoparticles (NPs) in drilling and completion fluids is still limited at the present time, in part because of a lack of consistent evidence for and clarification of NP interactions with rock formations, formation fluid, and other fluid additives. For instance, previous fluids research emphasized that NPs bring about pore plugging, which reduces pressure transmission and, in turn, fluid inflow, into the shale pore matrix, which ultimately helps stabilize the borehole. However, it is difficult to understand how pore plugging might be accomplished in the absence of any substantial filtration in shales, considering that the minimal permeability of shales does not allow for any appreciable Darcy flow. This paper addresses the crucial question: “How, when, and why do NPs plug shale pore throats?”
Zeta-potential (ZP) measurements were carried out on aqueous NP dispersions and on intact thin shale sections exposed to nanofluids to determine the degree of interaction behavior between NPs and shale. The experimental data were then used to calculate Derjaguin-Landau-Verwey-Overbeek (DLVO) curves (describing the force between charged surfaces interacting through a liquid medium) to determine if the total potential energy was sufficient for NPs to diffuse through the repellent barrier and attach to the shale surface. Calculated DLVO curves were used to demonstrate the NPs ability to contribute to borehole stability, but did not directly correlate the effects the NPs had on shale stability. Experiments, including pore pressure-transmission tests (PTTs), which measure fluid pressure penetration in shale, and modified thick-walled-cylinder (TWC) collapse tests, which explore the influence of NPs on the collapse pressure of shale samples, were conducted to directly investigate the effects of NPs on borehole stability in shale.
Our investigation showed that NPs can reduce fluid pressure penetration and delay borehole collapse in shale, but only under certain conditions. Electrostatic/electrodynamic interaction between NPs and shale surfaces, governed by DLVO forces, is the main mechanism that leads to pore-throat plugging, reducing pressure transmission, which in turn benefits borehole stability by slowing down near-wellbore pore-pressure elevation and effective-stress reduction. For Mancos Shale, 20-nm anionic nanosilica particles were effective in partially plugging the pore-throat system, depending on the pH of the nanofluid, which affects the surface potential and ZP of both NPs and shale. Furthermore, cationic nanosilica showed better results for pore-plugging capabilities than the anionic nanosilica.
Our findings lead to interesting challenges for the practical field application of NP-based drilling fluids for borehole stability, given that efficacy depends on the specific type of shale; the specific type, size, and concentration of NP; the interaction between NPs and shale; and external factors, such as pH, salinity, and temperature. Therefore, NP use for practical shale stabilization requires a dedicated, thoroughly engineered solution for each particular field application, and is unlikely to be “one size fits all.”
Peng, Sheng (University of Texas at Austin) | Ren, Bo (University of Texas at Austin) | Meng, Mianmo (China University of Petroleum, Beijing, and University of Texas at Austin)
Matrix permeability is a key petrophysical parameter in reservoir evaluation and simulation. However, measurement of this parameter remains problematic for unconventional reservoirs. One of the challenges lies in the influence of fractures. Inclusion of fractures can lead to overestimation of shale matrix permeability. In this paper, new experimental and data-analysis procedures are developed for more-accurate yet relatively fast measurement of shale matrix permeability on the basis of previous work (Peng and Loucks 2016). The influence of fractures on matrix porosity and permeability is quantified and excluded. Reliability and consistency of the measurement results are confirmed through multiple means, including analytical solution back calculation and measurements for similar samples but with different plug diameters. Because the influence of fractures is explicitly excluded in data analysis, the new method is also more flexible regarding sample conditions—even broken plug samples with fractures can be used in this method. This is another advantage of the new method given the difficulty in obtaining “intact” plugs because of the fissility of shale.