Computational Geosciences最新文献

The objective of this study is to propose a computationally inexpensive and effective approach that addresses the challenges faced with computationally expensive and time-consuming trial-and-error and direct optimization methods in well-control optimization. This approach involves combining proxy models such as artificial neural network (ANN) models with optimization algorithms to determine an optimal solution much faster. It was implemented in a heterogeneous oil reservoir undergoing waterflooding. The controllable parameters of the reservoir simulation model were identified as bottom-hole pressure for the producers and water injection rate for the injectors. Minimum and maximum values of each input parameter were defined based on reservoir conditions and used with a Box Behnken design (BBD) method to generate realizations for conducting reservoir simulations to obtain cumulative oil produced (COP) and cumulative water produced (CWP). The input and output data were normalized before being used for model development such that 70:15:15% of data was used for training, validation, testing, and all of the ANN model in which a coefficient of correlation (R) of 0.99756, 0.94354, 0.95813, and 0.98589 were obtained respectively. This indicates the accuracy, validity, and reliability of the model. The coefficient of determination (R²) for training, validation, testing, and all datasets as well as statistical error and trend analysis were used to validate the model. R² values for each case were not less than 0.80, and the responses were reproduced by the ANN model with average relative error and root mean square error of not more than 0.7%. Weights and biases were extracted from the trained and validated ANN model to aid in outputting a visible ANN model that can be used for optimization studies. A multi-objective genetic algorithm was used to determine an optimal solution that maximized COP and minimized CWP. Average and optimized input data were used to run the developed ANN model. Results revealed that the optimized case outperformed the case for which average input values were used evidenced by the production of 34.198 MSm³ more oil and 14.297 MMSm³ less water. The findings of this study showed that using an ANN-MOGA approach will eliminate the computationally expensive, time-consuming, and inefficient trial-and-error approach for well-control optimization. Oil recovery was improved while water production was reduced resulting in low expenditure on treatment and disposal of produced water.

In the process of harnessing geothermal energy, the enduring effects of thermal cycling on granite within the geothermal reservoir led to alterations in rock permeability. This, in turn, directly impacts the efficiency of thermal energy extraction. Hence, delving into the micro-permeability dynamics of granite is imperative to understand the characteristics of prevalent fractures. Employing micro-CT technology, we meticulously extract and analyze the pores of granite samples, unveiling the distribution patterns of pores and micro-permeability variations under successive thermal cycles. The resultant three-dimensional pore model vividly showcases the evolving pore structures during both heating and cooling cycles. Notably, the distribution curve of granite pore volume adheres to a chi-square distribution. Through the utilization of pore volume distribution curves, we categorize rock pores into four distinct types: micropores, mesopores, macropores, and fractures. Both quantitatively and visually, micropores and mesopores predominate, while a fraction of pores gradually transitions into sizable fractures. By employing suitable representative elements to construct the flow field within the large pore model and subsequently calculating permeability, we observe a positive correlation between porosity, permeability, and cyclic temperature-induced damage. Notably, the estimated permeability closely aligns with the measured values, exhibiting an acceptable margin of error. Furthermore, under the influence of thermal cycle-induced damage, the flow simulation demonstrates a noticeable increase in the number of flow lines, consequently resulting in enhanced permeability. This effectively validates the accuracy of the flow simulation based on micro-CT results.

Complex geological structures like pores, fractures and faults in submarine hydrothermal reservoirs have significant but unclear effects on internal hydrothermal flow and heat transfer, which hinders reservoir exploitation. This study establishes a heat transfer model of a buried pipe coupled in fracture-porous media based on the reservoir characteristics. The model is verified through experiments using fractured porous media test rigs and computational fluid dynamics simulations. Simulations are performed to investigate the effects of fracture flow velocity, width, cornerstone porosity on the heat transfer efficiency of the buried pipe. Results show that optimizing fracture flow velocity, fracture width and cornerstone porosity can substantially improve the heat transfer performance of the buried pipe. Increasing fracture flow velocity from 10^–4 m/s to 10^–3 m/s, results in a 161.92% increase of Nusselt number. When the fracture width increases to 5 times the pipe diameter, Nusselt number rises by 35.52%. The heat transfer is optimal at a porosity of 0.3. This study provides theoretical guidance for exploiting submarine hydrothermal resources and designing fracture-porous couplings to enhance buried pipe heat transfer.

Surface tension affects all aspects of fluid flow in porous media. Through measurements of surface tension interaction under multiphase conditions, a relative permeability curve can be determined. Relative permeability is a numerical description of the interaction between two or more fluids and the porous media. It is a critical parameter for various tools that characterize subsurface multiphase flow systems, such as numerical simulation for carbon sequestration, oil and gas development, and groundwater contamination remediation. Therefore, it is critical to get a good statistical distribution of relative permeability in the porous media under study. Empirical formula for determining relative permeability from capillary pressure are already well established but do not provide the needed flexibility that is required to match laboratory-derived relative permeability curves. By expanding the existing methods for calculating relative permeability from capillary pressure data, it is possible to create both two and three-phase relative permeability curves. Mercury intrusion capillary pressure (MICP) data from the Morrow 'B' Sandstone coupled with interfacial tension and contact angle measurements were used to create a suite of relative permeability curves. These curves were then calibrated to a small sample of existing laboratory curves to elucidate common fitting parameters for the formation that were then used to create relative permeability curves from MICP data that does not have an associated laboratory-measured relative permeability curve.

Numerical simulation of multiphase flow in porous media is an important step in understanding the dynamic behavior of geologic CO(_2) storage (GCS). Scaling up GCS requires fast and accurate high-resolution modeling of the storage reservoir pressure and saturation plume migration; however, such modeling is challenging due to the high computational costs of traditional physics-based simulations. Deep learning models trained with numerical simulation data can provide a fast and reliable alternative to expensive physics-based numerical simulations. We propose a Stochastic pix2vid neural network architecture for solving multiphase fluid flow problems with significant speed, accuracy, and efficiency. The Stochastic pix2vid model is designed based on the principles of computer vision and video synthesis and is able to generate dynamic spatiotemporal predictions of fluid flow from static reservoir models, closely mimicking the performance of traditional numerical simulation. We apply the Stochastic pix2vid model to a highly-complex CO(_2)-water multiphase problem with a wide range of reservoir models in terms of porosity and permeability heterogeneity, facies distribution, and injection configurations. The Stochastic pix2vid method is first-of-its-kind in static-to-dynamic prediction of reservoir behavior, where a single static input is mapped to its dynamic response with a fixed number of timesteps. The Stochastic pix2vid method provides notable performance in highly heterogeneous geologic formations and complex estimation such as CO(_2) saturation and pressure buildup plume determination. The trained model can serve as a general-purpose, static-to-dynamic (image-to-video) alternative to traditional numerical reservoir simulation of 2D CO(_2) injection problems with up to 6,500(times ) speedup compared to traditional numerical simulation using the MATLAB Reservoir Simulation Toolbox.

I consider the problem of model diagnostics, that is, the problem of criticizing a model prior to history matching by comparing data to an ensemble of simulated data based on the prior model (prior predictions). If the data are not deemed as a credible prior prediction by the model diagnostics, some settings of the model should be changed before history matching is attempted. I particularly target methodologies that are computationally feasible for large models with large amounts of data. A multiscale methodology, that can be applied to analyze differences between data and prior predictions in a scale-by-scale fashion, is proposed for this purpose. The methodology is computationally inexpensive, straightforward to apply, and can handle correlated observation errors without making approximations. The multiscale methodology is tested on a set of toy models, on two simplistic reservoir models with synthetic data, and on real data and prior predictions from the Norne field. The tests include comparisons with a previously published method (termed the Mahalanobis methodology in this paper). For the Norne case, both methodologies led to the same decisions regarding whether to accept or discard the data as a credible prior prediction. The multiscale methodology led to correct decisions for the toy models and the simplistic reservoir models. For these models, the Mahalanobis methodology either led to incorrect decisions, and/or was unstable with respect to selection of the ensemble of prior predictions.

Markov chain geostatistics is a methodology for simulating categorical fields. Its fundamental model for conditional simulation is the Markov chain random field (MCRF) model, with the transiogram serving as its basic spatial correlation measure. There are different methods to obtain transiogram models for MCRF simulation based on sample data and expert knowledge: linear interpolation, mathematical model joint-fitting, and a mixed approach combining both. This study aims to explore the sensitivity of the MCRF model to different transiogram jointing modeling methods. Two case studies were conducted to examine how simulated results, including optimal prediction maps and simulated realization maps, vary with different sets of transiogram models. The results indicate that all three transiogram joint modeling methods are applicable, and the MCRF model exhibits a general insensitivity to transiogram models produced by different methods, particularly when sample data are sufficient to generate reliable experimental transiograms. The variations in overall simulation accuracies based on different sets of transiogram models are not significant. However, notable improvements in simulation accuracy for minor classes were observed when theoretical transiogram models (generated by mathematical model fitting with expert knowledge) were utilized. This study suggests that methods for deriving transiogram models from experimental transiograms perform well in conditional simulations of categorical soil variables when meaningful experimental transiograms can be estimated. Employing mathematical models for transiogram modeling of minor classes provides a way to incorporate expert knowledge and improve the simulation accuracy of minor classes.

In this work, various high-accuracy numerical schemes for transport problems in fractured media are further developed and compared. Specifically, to capture sharp gradients and abrupt changes in time, schemes with low order of accuracy are not always sufficient. To this end, discontinuous Galerkin up to order two, Streamline Upwind Petrov-Galerkin, and finite differences, are formulated. The resulting schemes are solved with sparse direct numerical solvers. Moreover, time discontinuous Galerkin methods of order one and two are solved monolithically and in a decoupled fashion, respectively, employing finite elements in space on locally refined meshes. Our algorithmic developments are substantiated with one regular fracture network and several further configurations in fractured media with large parameter contrasts on small length scales. Therein, the evaluation of the numerical schemes and implementations focuses on three key aspects, namely accuracy, monotonicity, and computational costs.

Identifying the source of an oil spill is an essential step in environmental forensics. The Bayesian approach allows to estimate the source parameters of an oil spill from available observations. Sampling the posterior distribution, however, can be computationally prohibitive unless the forward model is replaced by an inexpensive surrogate. Yet the construction of globally accurate surrogates can be challenging when the forward model exhibits strong nonlinear variations. We present an iterative data-driven algorithm for the construction of polynomial chaos surrogates whose accuracy is localized in regions of high posterior probability. Two synthetic oil spill experiments, in which the construction of prior-based surrogates is not feasible, are conducted to assess the performance of the proposed algorithm in estimating five source parameters. The algorithm successfully provided a good approximation of the posterior distribution and accelerated the estimation of the oil spill source parameters and their uncertainties by an order of 100 folds.

This work proposes a novel meshing technique that is able to extract surfaces from processed seismic data and integrate surfaces that were constructed using other extraction techniques. Contrary to other existing methods, the process is fully automated and does not require any user intervention. The proposed system includes an approach for closing the gaps that arise from the different techniques used for surface extraction. The developed process is able to handle non-manifold domains that result from multiple surface intersections. Surface and volume meshing that comply with user specified mesh control techniques are implemented to ensure the desired mesh quality. The integrated procedures provide a unique facility to handle geotechnical models and accelerate the generation of quality meshes for geophysics modelling. The developed procedure enables the creation of meshes for complex reservoir models to be reduced from weeks to a few hours. Various industrial examples are shown to demonstrate the practicable use of the developed approach to handle real life data.