Compositional simulators conventionally use Li's correlation to approximate the critical temperature (
We here propose to replace Li's correlation by a rigorous calculation of
In order to address the observation that gas/oil relative permeability curves tend to straight lines when approaching to the critical point, a second level of interpolation with respect to the IFT is applied within the phase envelope between miscible and immiscible three-phase models. Continuity is, by construction, guaranteed at any possible phase-state transition.
The proposed relative permeability model is first tested standalone (i.e., on a single cell) with different hydrocarbon mixtures, by analysis of the dependent parameter (true or fictitious IFT) and the relative permeabilities at different
The model is secondly implemented in our In-House Research Reservoir Simulator (IHRRS), and tested on a synthetic 2D cross-section undergoing near-critical gas injection. We observe that with conventional models based on Li's correlation, discontinuities in the relative permeability model when crossing the phase envelope occur, as well as spurious phase flipping. No such unphysical behavior is observed with the proposed approach, while requiring the same input data.
There is of course a computational cost involved in properly calculating
A well-designed pilot is instrumental in reducing uncertainty for the full-field implementation of improved oil recovery (IOR) operations. Traditional model-based approaches for brown-field pilot analysis can be computationally expensive as it involves probabilistic history matching first to historical field data and then to probabilistic pilot data. This paper proposes a practical approach that combines reservoir simulations and data analytics to quantify the effectiveness of brown-field pilot projects.
In our approach, an ensemble of simulations are first performed on models based on prior distributions of subsurface uncertainties and then results for simulated historical data, simulated pilot data and ob jective functions are assembled into a database. The distribution of simulated pilot data and ob jective functions are then conditioned to actual field data using the Data-Space Inversion (DSI) technique, which circumvents the difficulties of traditional history matching. The samples from DSI, conditioned to the observed historical data, are next processed using the Ensemble Variance Analysis (EVA) method to quantify the expected uncertainty reduction of ob jective functions given the pilot data, which provides a metric to ob jectively measure the effectiveness of the pilot and compare the effectiveness of different pilot measurements and designs. Finally, the conditioned samples from DSI can also be used with the classification and regression tree (CART) method to construct signpost trees, which provides an intuitive interpretation of pilot data in terms of implications for ob jective functions.
We demonstrate the practical usefulness of the proposed approach through an application to a brown-field naturally fractured reservoir (NFR) to quantify the expected uncertainty reduction and Value of Information (VOI) of a waterflood pilot following more than 10 years of primary depletion. NFRs are notoriously hard to history match due to their extreme heterogeneity and difficult parameterization; the additional need for pilot analysis in this case further compounds the problem. Using the proposed approach, the effectiveness of a pilot can be evaluated, and signposts can be constructed without explicitly history matching the simulation model. This allows ob jective and efficient comparison of different pilot design alternatives and intuitive interpretation of pilot outcomes. We stress that the only input to the workflow is a reasonably sized ensemble of prior simulations runs (about 200 in this case), i.e., the difficult and tedious task of creating history-matched models is avoided. Once the simulation database is assembled, the data analytics workflow, which entails DSI, EVA, and CART, can be completed within minutes.
To the best of our knowledge, this is the first time the DSI-EVA-CART workflow is proposed and applied to a field case. It is one of the few pilot-evaluation methods that is computationally efficient for practical cases. We expect it to be useful for engineers designing IOR pilot for brown fields with complex reservoir models.
Tian, Ye (Colorado School of Mines) | Xiong, Yi (Colorado School of Mines) | Wang, Lei (Colorado School of Mines) | Lei, Zhengdong (Research Institute of Petroleum Exploration and Development, PetroChina) | Zhang, Yuan (Research Institute of Petroleum Exploration and Development, PetroChina) | Yin, Xiaolong (Colorado School of Mines) | Wu, Yu-Shu (Colorado School of Mines)
Gas injection has become the top choice for IOR/EOR pilots in tight oil reservoirs because of its high injectivity. The effects of nanoconfinement and geomechanics are generally considered as non-negligible, but its coupled effects and resulting flow and displacement are still not well understood for gas injection. We hence present a general compositional model and simulator to investigate the complicated multiphase and multicomponent behaviors during gas injection in tight oil reservoirs.
This compositional model is able to account for vital physics in unconventional reservoirs, including nanopore confinement, molecular diffusion, rock-compaction, and non-Darcy flow. The MINC method is implemented to handle fractured media. The nanopore confinement effect is modeled by including capillarity in VLE calculations. The rock compaction effect is represented by solving the mean stress from a governing geomechanical equation which is fully coupled with the mass balance equations to ensure the numerical stability as well as a physically correct solution. The equations are discretized with integral finite difference method and then solved numerically by Newton's method.
The simulator is validated against a commercial compositional software (CMG-GEM) before it is applied to simulate gas injection. Huff-n-puff with dry gas in Eagle Ford is investigated. The simulation result shows that if the reservoir pressure is much higher than the bubble point pressure, the nanopore confinement effect will have a minimal impact on the recovery factor (RF) for both the depletion and the first few cycles of gas huff-n-puff. Geomechanics is found to be an influencing factor on RF but not always in a detrimental way, as enhanced rock compaction drive could offset the reduction of permeability in certain scenarios. Gas huff-n-puff would improve the RF of each component compared with the depletion. The heavy component would first have a higher recovery than the light component at the first few cycles of huff-n-puff, but its RF will be outpaced by the light component when the gas saturation in the matrix surpasses the critical gas saturation. Lastly, considering the nanopore confinement effects would slightly reduce the RF of the light component but increase the RF of the heavy component after huff-n-puff when combined with the critical gas saturation effect in the matrix.
This study presents a 3D multiphase, multicomponent simulator which is a practical tool for accurately modeling of primary depletion as well as gas injection IOR/EOR processes in unconventional oil reservoirs. This simulator is not only of great importance for assisting researchers to understand complex multiphase and multicomponent behaviors in tight oil production but also of great use for engineers to optimize gas injection parameters in field applications.
In this work, we present the development of a compositional simulator accelerated by proxy flash calculation. We aim to speed up the compositional modeling of unconventional formations by stochastic training.
We first developed a standalone vapor-liquid flash calculation module with the consideration of capillary pressure and shift of critical properties induced by confinement. We then developed a fully connected network with 3 hidden layers using Keras. The network is trained with Adam optimizer. 250,000 samples are used as training data, while 50,000 samples are used as testing data. Based on the trained network, we developed a forward modeling (prediction) module in a compositional simulator. Therefore, during the simulation run, the phase behavior of the multicomponent system within each grid block at each iteration is obtained by simple interpolation from the forward module.
Our standalone flash calculation module matches molecular simulation results well. The accuracy of the trained network is up to 97%. With the implementation of the proxy flash calculation module, the CPU time is reduced by more than 30%. In the compositional simulator, less than 2% of CPU time is spent in the proxy flash calculation.
The novelty of this work lies in two aspects. We have incorporated the impacts of both capillary pressure and shift of critical properties in the flash calculation, which matches molecular simulation results well. We developed a proxy flash calculation module and implemented it in a compositional simulator to replace the traditional flash calculation module, speeding the simulation by 30%.
An XFEM-EDFM scheme and associated monolithic solution method are proposed to model time-dependent poromechanics and two-phase flow. Fractures are modeled as interfaces with displacement discontinuities. The contact forces are treated using Lagrange Multipliers. A number of numerical tests are performed to investigate the Newmark scheme's accuracy and cases for wave propagation in poroelastic and natural fracture media are implemented to evaluate computational efficiency. We apply the method to model seismic data from hydraulic fracture network. Empirical results validate the Newmark scheme accuracy as well as computational efficiency and localization of newton update in seismic field is necessary for the further application. The synthetic model of multiple hydraulic stages illustrates the effect of flow coupling and newly generated fractures on the microseismic field. The model is applied to simultaneously assimilate well performance and microseismic observations, thereby informing about the causal event dynamics.
Calibrating production and economic forecasts (objective functions) to observed data is a key component in oil and gas reservoir management. Traditional model-based data assimilation (history matching) entails first calibrating models to the data and then using the calibrated models for probabilistic forecast, which is often ill-posed and time-consuming. In this study, we present an efficient regression-based approach that directly predicts the objectives conditioned to observed data without model calibration.
In the proposed workflow, a set of samples is drawn from the prior distribution of the uncertainty parameter space, and simulations are performed on these samples. The simulated data and values of the objective functions are then assembled into a database, and a functional relationship between the perturbed simulated data (simulated data plus error) and the objective function is established through nonlinear regression methods such as nonlinear partial least square (NPLS) with automatic parameter selection. The prediction from this regression model provides estimates for the mean of the posterior distribution. The posterior variance is estimated by a localization technique.
The proposed methodology is applied to a data assimilation problem on a field-scale reservoir model. The posterior distributions from our approach are validated with reference solution from rejection sampling and then compared with a recently proposed method called ensemble variance analysis (EVA). It is shown that EVA, which is based on a linear-Gaussian assumption, is equivalent to simulation regression with linear regression function. It is also shown that the use of NPLS regression and localization in our proposed workflow eliminates the numerical artifact from the linear-Gaussian assumption and provides substantially better prediction results when strong nonlinearity exists. Systematic sensitivity studies have shown that the improvement is most dramatic when the number of training samples is large and the data errors are small.
The proposed nonlinear simulation-regression procedure naturally incorporates data error and enables the estimation of the posterior variance of objective quantities through an intuitive localization approach. The method provides an efficient alternative to traditional two-step approach (probabilistic history matching and then forecast) and offers improved performance over other existing methods. In addition, the sensitivity studies related to the number of training runs and measurement errors provide insights into the necessity of introducing nonlinear treatments in estimating the posterior distribution of various objective quantities.
The aim of this study is to determine to what extent the quality of a history matched model is a good predictor of future production. The background is the common assumption that the better a model matches the production data is the better it is for forecasting, or, at the very least, it leads to an improved estimate of the uncertainty in future production. We demonstrate that the validity of this assumption depends on the length of the history match period and that of the forecasting period. It also depends on how heterogeneous the reservoir is.
The correlation between the quality of history match and quality of forecast depends on various factors. For the same level of heterogeneity one of the strongest factors is the water breakthrough time for the base and compared cases.
Broadly if both the base and compared case have water breakthrough before the end of the history match period then the forecasts are reasonable. However, there appears to be a very rapid transition from a reasonably good history match leading to a good forecast to a moderately good history match leading to a very poor forecast. If water breakthrough has not occurred there is a very poor correlation between the quality of the history match and the quality of the forecast. So, the traditional belief that a good history matched model will also produce a good forecast is not always true.
Magzymov, Daulet (John and Willie Leone Family Department of Energy and Mineral Engineering and The EMS Energy Institute, The Pennsylvania State University) | Purswani, Prakash (John and Willie Leone Family Department of Energy and Mineral Engineering and The EMS Energy Institute, The Pennsylvania State University) | Karpyn, Zuleima T. (John and Willie Leone Family Department of Energy and Mineral Engineering and The EMS Energy Institute, The Pennsylvania State University) | Johns, Russell T. (John and Willie Leone Family Department of Energy and Mineral Engineering and The EMS Energy Institute, The Pennsylvania State University)
The objective of this paper is to model low-salinity waterflooding by honoring physico-chemical complexity, namely, the effects of reaction kinetics and dispersion. Recent literature provides evidence for the potential of low-salinity water injection to improve oil recovery through wettability alteration through a complex network of reactions. However, there is lack of consensus with respect to the exact chemical species that are responsible for the alteration process. Therefore, in this study, we develop a a simplified binary multiphase reactive transport model that honors the general surface reaction for wettability alteration, but at the same time includes effects of reaction kinetics and dispersion in the governing equations.
We lump the reactants, such as sodium, calcium, and petroleum acids, into two characteristic components based on their contribution to oil or water wetness. The wettability alteration process is modelled as a competition between these primary characteristic components to occupy the rock surface as described by reaction kinetics.
The simulation results show a significant impact of reaction kinetics on the rate of wettability alteration compared to assuming instantaneous equilibrium. In the limiting case of a very slow reaction rate (Da ~ 0), low-salinity injection does not alter the wettability. Particularly, no effect on ultimate oil recovery is observed, regardless of the injected salinity level. For the case of an instantaneous reaction the ultimate oil recovery is sensitive to the injected fluid salinity. Moreover, during fast reactions (Da ~ 10-4), the wettability alteration front moves slower than the injected fluid front caused by excess salt in the solution that desorbs from the rock surface. The delay in wettability alteration is crucial to consider for an appropriate slug size design of low-salinity injection. Lastly, we observe that dispersion does not affect the ultimate oil recovery during wettability alteration as compared to reaction kinetics.
In this paper we present our results, challenges and learnings, over a two-year period wherein robust multiobjective optimization was applied at the Mariner asset which is being currently developed. Many different problems were solved with different objectives. These problems were formulated based on the phases of planning and development at the asset. The optimization problems include drilling order and well trajectory optimization as the main objectives with reduction in water cut and reduction of gas production to minimize flaring as secondary objectives. We use the efficient stochastic gradient technique, StoSAG, to achieve optimization incorporating geological and petrophysical uncertainty. For some problems computational limitations introduced challenges while for other problems operational constraints introduced challenges for the optimization. Depending on the problems significant increases between 5% and 20% in the expected value of the objective function were achieved. For the multi-objective optimization cases we show that nontrivial optimal strategies are obtained which significantly reduce (40% decrease) gas production with minimal loss (less than 1%) in the economic objective. Our results illustrate the importance of flexible optimizations workflows to achieve results of significant practical value at different stages of the planning and development cycle at an operational asset.
Fracture propagation (FP) occurs in extensive applications including hydraulic fracturing, underground disposal of liquid waste, CO2 sequestration etc. It is crucial to develop a simulator that is able to reflect physics behind FP and capture the FP path. This work is an extension of the previous developed model (Ren et al. (2018), Ren & Younis (2018)) to the simulation of FP. One of the remarkable benefits using the coupled XFEM-EDFM scheme allows FP free of the remeshing. In this work, the onset of FP is controlled by a single parameter, the equivalent stress intensity factor (SIF). A domain integral method, J integral is applied to extract the SIF information. A time marching scheme is performed to ensure the SIF criterion satisfied everytime fracture propagates. The developed simulator is verified by the analytical solutions and shows the capability of FP simulation in poroelastic materials.