A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

        1 2 3 | Last   [Sort alphabetically]   [Restore default list]

  Subjects -> GEOGRAPHY (Total: 493 journals)
Showing 1 - 200 of 277 Journals sorted by number of followers
Geophysical Research Letters     Full-text available via subscription   (Followers: 184)
Journal of Geophysical Research : Space Physics     Full-text available via subscription   (Followers: 159)
Journal of Geophysical Research : Atmospheres     Partially Free   (Followers: 149)
Journal of Geophysical Research : Planets     Full-text available via subscription   (Followers: 143)
Remote Sensing of Environment     Hybrid Journal   (Followers: 96)
Antipode     Hybrid Journal   (Followers: 65)
Journal of Geophysical Research : Earth Surface     Partially Free   (Followers: 60)
Journal of Geophysical Research : Oceans     Partially Free   (Followers: 60)
Progress in Human Geography     Hybrid Journal   (Followers: 60)
Journal of Geophysical Research : Solid Earth     Full-text available via subscription   (Followers: 58)
International Journal of Geographical Information Science     Hybrid Journal   (Followers: 56)
GIScience & Remote Sensing     Open Access   (Followers: 54)
Journal of Water and Climate Change     Open Access   (Followers: 53)
Climate Change Economics     Hybrid Journal   (Followers: 50)
Reviews of Geophysics     Full-text available via subscription   (Followers: 49)
Remote Sensing Letters     Hybrid Journal   (Followers: 45)
Annals of the American Association of Geographers     Hybrid Journal   (Followers: 43)
Economic Geography     Hybrid Journal   (Followers: 42)
Applied Geography     Hybrid Journal   (Followers: 39)
Climate and Development     Hybrid Journal   (Followers: 34)
Urban Geography     Hybrid Journal   (Followers: 34)
Journal of Geophysical Research : Biogeosciences     Full-text available via subscription   (Followers: 34)
Geochemistry, Geophysics, Geosystems     Full-text available via subscription   (Followers: 34)
Annals of GIS     Open Access   (Followers: 31)
Journal of Coastal Research     Hybrid Journal   (Followers: 31)
Cartography and Geographic Information Science     Hybrid Journal   (Followers: 31)
GPS Solutions     Hybrid Journal   (Followers: 28)
Transactions of the Institute of British Geographers     Hybrid Journal   (Followers: 27)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 24)
Journal of the Middle East and Africa     Hybrid Journal   (Followers: 23)
Dialogues in Human Geography     Hybrid Journal   (Followers: 20)
China : An International Journal     Full-text available via subscription   (Followers: 20)
Urban Research & Practice     Hybrid Journal   (Followers: 20)
Imago Mundi: The International Journal for the History of Cartography     Hybrid Journal   (Followers: 20)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 19)
Atmospheric Measurement Techniques (AMT)     Open Access   (Followers: 19)
Water International     Hybrid Journal   (Followers: 19)
Journal of the American Planning Association     Hybrid Journal   (Followers: 19)
Geography Compass     Hybrid Journal   (Followers: 18)
Journal of Cultural Geography     Hybrid Journal   (Followers: 18)
Cartographica : The International Journal for Geographic Information and Geovisualization     Full-text available via subscription   (Followers: 17)
Professional Geographer     Hybrid Journal   (Followers: 17)
Africa Insight     Full-text available via subscription   (Followers: 16)
Crossings : Journal of Migration & Culture     Hybrid Journal   (Followers: 16)
The Geographical Journal     Hybrid Journal   (Followers: 16)
International Geology Review     Hybrid Journal   (Followers: 16)
Computational Geosciences     Hybrid Journal   (Followers: 15)
Tectonics     Full-text available via subscription   (Followers: 15)
Geomatics, Natural Hazards and Risk     Open Access   (Followers: 14)
American Journal of Geographic Information System     Open Access   (Followers: 14)
Annual Review of Marine Science     Full-text available via subscription   (Followers: 13)
Buildings & Landscapes: Journal of the Vernacular Architecture Forum     Full-text available via subscription   (Followers: 13)
Progress in Physical Geography     Hybrid Journal   (Followers: 13)
International Indigenous Policy Journal     Open Access   (Followers: 13)
Geographical Review     Hybrid Journal   (Followers: 13)
Bulletin of Geosciences     Open Access   (Followers: 12)
Geographical Analysis     Hybrid Journal   (Followers: 11)
Journal of Geography     Hybrid Journal   (Followers: 11)
Geosciences Journal     Hybrid Journal   (Followers: 11)
Geographical Research     Hybrid Journal   (Followers: 11)
Canadian Journal of Soil Science     Full-text available via subscription   (Followers: 11)
GeoJournal     Hybrid Journal   (Followers: 11)
American Journal of Human Ecology     Open Access   (Followers: 11)
Geography and Natural Resources     Hybrid Journal   (Followers: 10)
European Spatial Research and Policy     Open Access   (Followers: 9)
Cartographic Journal     Hybrid Journal   (Followers: 9)
Atmospheric Measurement Techniques Discussions (AMTD)     Open Access   (Followers: 9)
Journal of Iberian and Latin American Research     Hybrid Journal   (Followers: 8)
Physical Geography     Hybrid Journal   (Followers: 8)
International Journal of Health Geographics     Open Access   (Followers: 8)
Natural Science     Open Access   (Followers: 8)
Middle East Development Journal     Hybrid Journal   (Followers: 8)
Journal of Borderlands Studies     Hybrid Journal   (Followers: 8)
Journal of Geographical Systems     Hybrid Journal   (Followers: 8)
International Journal of Applied Geospatial Research     Hybrid Journal   (Followers: 8)
Urban History Review / Revue d'histoire urbaine     Full-text available via subscription   (Followers: 7)
Journal of Latin American Geography     Full-text available via subscription   (Followers: 7)
California Italian Studies Journal     Full-text available via subscription   (Followers: 7)
Geo-spatial Information Science     Open Access   (Followers: 7)
Nordic Journal of Migration Research     Open Access   (Followers: 7)
Journal of Maps     Open Access   (Followers: 7)
Social Geography Discussions (SGD)     Open Access   (Followers: 7)
GeoInformatica     Hybrid Journal   (Followers: 7)
Northern Scotland     Hybrid Journal   (Followers: 6)
Asia Policy     Full-text available via subscription   (Followers: 6)
Australian Geographer     Hybrid Journal   (Followers: 6)
Singapore Journal of Tropical Geography     Hybrid Journal   (Followers: 6)
Ocean Science Journal     Hybrid Journal   (Followers: 6)
The Canadian Geographer/le Geographe Canadien     Hybrid Journal   (Followers: 6)
Creativity Studies     Open Access   (Followers: 5)
Australian Antarctic Magazine     Free   (Followers: 5)
Focus on Geography     Partially Free   (Followers: 5)
Journal of Developmental Entrepreneurship     Hybrid Journal   (Followers: 5)
Asian Geographer     Hybrid Journal   (Followers: 5)
Current Research in Geoscience     Open Access   (Followers: 5)
Journal of Australian Studies     Hybrid Journal   (Followers: 5)
ISPRS International Journal of Geo-Information     Open Access   (Followers: 5)
Journal of Map & Geography Libraries     Hybrid Journal   (Followers: 5)
Transmodernity : Journal of Peripheral Cultural Production of the Luso-Hispanic World     Open Access   (Followers: 4)
Applied Geomatics     Hybrid Journal   (Followers: 4)
Latinoamérica. Revista de estudios Latinoamericanos     Open Access   (Followers: 4)
Bulletin of the Ecological Society of America     Open Access   (Followers: 4)
Geografiska Annaler, Series A : Physical Geography     Hybrid Journal   (Followers: 4)
Globe, The     Full-text available via subscription   (Followers: 4)
Genre & histoire     Open Access   (Followers: 4)
Journal of Sedimentary Research     Hybrid Journal   (Followers: 4)
Southeastern Europe     Hybrid Journal   (Followers: 3)
Bulletin of Geography. Socio-economic Series     Open Access   (Followers: 3)
Limnological Review     Open Access   (Followers: 3)
Interaction     Full-text available via subscription   (Followers: 3)
Journal of Western Archives     Open Access   (Followers: 3)
Économie rurale     Open Access   (Followers: 3)
Social Dynamics: A journal of African studies     Hybrid Journal   (Followers: 3)
New Zealand Journal of Geography     Hybrid Journal   (Followers: 3)
Journal of Burma Studies     Full-text available via subscription   (Followers: 3)
South Asian Diaspora     Hybrid Journal   (Followers: 3)
All Earth     Open Access   (Followers: 3)
Lithosphere     Open Access   (Followers: 3)
International Journal of Image and Data Fusion     Hybrid Journal   (Followers: 3)
Polar Research     Open Access   (Followers: 3)
History of Geo- and Space Sciences     Open Access   (Followers: 2)
Journal of Earthquake and Tsunami     Hybrid Journal   (Followers: 2)
Pastoralism : Research, Policy and Practice     Open Access   (Followers: 2)
Standort - Zeitschrift für angewandte Geographie     Hybrid Journal   (Followers: 2)
Norois     Open Access   (Followers: 2)
Geodesy and Cartography     Open Access   (Followers: 2)
Eastern European Countryside     Open Access   (Followers: 2)
Mineralogia     Open Access   (Followers: 2)
Regions and Cohesion     Open Access   (Followers: 2)
Polar Geography     Hybrid Journal   (Followers: 2)
Southeastern Geographer     Full-text available via subscription   (Followers: 2)
BioRisk     Open Access   (Followers: 2)
Norsk Geografisk Tidsskrift - Norwegian Journal of Geography     Hybrid Journal   (Followers: 2)
Scottish Geographical Journal     Hybrid Journal   (Followers: 2)
Geosphere     Open Access   (Followers: 2)
Études rurales     Open Access   (Followers: 2)
Yearbook of the Association of Pacific Coast Geographers     Full-text available via subscription   (Followers: 2)
Polar Journal     Hybrid Journal   (Followers: 2)
Newfoundland and Labrador Studies     Full-text available via subscription   (Followers: 2)
Regional Science Policy & Practice     Hybrid Journal   (Followers: 2)
Provincial China     Hybrid Journal   (Followers: 2)
Geographical Education     Full-text available via subscription   (Followers: 2)
Cahiers franco-canadiens de l'Ouest     Full-text available via subscription   (Followers: 2)
The South Asianist     Open Access   (Followers: 2)
Reflets : revue d'intervention sociale et communautaire     Full-text available via subscription   (Followers: 2)
Maine Policy Review     Open Access   (Followers: 2)
Revista de Geografía Norte Grande     Open Access   (Followers: 1)
Geoforum Perspektiv     Open Access   (Followers: 1)
Estudios Geográficos     Open Access   (Followers: 1)
PRISM : A Journal of Regional Engagement     Open Access   (Followers: 1)
Norteamérica     Open Access   (Followers: 1)
Amerika     Open Access   (Followers: 1)
L'Année du Maghreb     Open Access   (Followers: 1)
Indiana     Open Access   (Followers: 1)
Les Cahiers d'Outre-Mer     Open Access   (Followers: 1)
Journal of the Southwest     Full-text available via subscription   (Followers: 1)
Revue archéologique du Centre de la France     Open Access   (Followers: 1)
Journal of Terrestrial Observation     Open Access   (Followers: 1)
Méditerranée     Open Access   (Followers: 1)
Journal de la Société des Océanistes     Open Access   (Followers: 1)
Geochronometria     Open Access   (Followers: 1)
South African Geographical Journal     Hybrid Journal   (Followers: 1)
GEM - International Journal on Geomathematics     Hybrid Journal   (Followers: 1)
Terrae Incognitae     Hybrid Journal   (Followers: 1)
International Journal of Bahamian Studies     Open Access   (Followers: 1)
Études internationales     Full-text available via subscription   (Followers: 1)
Recherches sociographiques     Full-text available via subscription   (Followers: 1)
Physio-Géo     Open Access   (Followers: 1)
GEOMATICA     Hybrid Journal   (Followers: 1)
European Countryside     Open Access   (Followers: 1)
PSC Discussion Papers Series     Open Access  
Anales de Geografía de la Universidad Complutense     Open Access  
International Journal of River Basin Management     Hybrid Journal  
Revista Geográfica de América Central     Open Access  
Multiciencias     Open Access  
Investigaciones Geográficas (Esp)     Open Access  
Sociedade & Natureza     Open Access  
Región y Sociedad     Open Access  
Migración y Desarrollo     Open Access  
Migraciones Internacionales     Open Access  
Investigaciones Geográficas     Open Access  
Frontera Norte     Open Access  
Cuadernos de Desarrollo Rural     Open Access  
Boletim de Ciências Geodésicas     Open Access  
Territoire en Mouvement     Open Access  
Quaestiones Geographicae     Open Access  
Limes. Cultural Regionalistics     Open Access  
Preview     Hybrid Journal  
Cuadernos de Geografía : Revista Colombiana de Geografía     Open Access  
Studia Universitatis Babes-Bolyai, Geologia     Open Access  
Recherches amérindiennes au Québec     Full-text available via subscription  
Rabaska : revue d'ethnologie de l'Amérique française     Full-text available via subscription  
Port Acadie : revue interdisciplinaire en études acadiennes / Port Acadie: An Interdisciplinary Review in Acadian Studies     Full-text available via subscription  
Études/Inuit/Studies     Full-text available via subscription  
Aurora Journal     Full-text available via subscription  
Revista de la Asociacion Geologica Argentina     Open Access  
San Francisco Estuary and Watershed Science     Open Access  
Journal of Alpine Research : Revue de géographie alpine     Open Access  
Géocarrefour     Open Access  
Confins     Open Access  

        1 2 3 | Last   [Sort alphabetically]   [Restore default list]

Similar Journals
Journal Cover
Computational Geosciences
Journal Prestige (SJR): 0.985
Citation Impact (citeScore): 3
Number of Followers: 15  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1573-1499 - ISSN (Online) 1420-0597
Published by Springer-Verlag Homepage  [2469 journals]
  • Lithology identification in carbonate thin section images of the Brazilian
           pre-salt reservoirs by the computational vision and deep learning

    • Free pre-print version: Loading...

      Abstract: Abstract Currently, the computer vision area, which represents one of the subfields of artificial intelligence and machine learning, has been widely used to process data in the oil and gas industry. In this context, the detection of specific properties inside carbonate rocks in different datasets from petroleum reservoirs represents a considerable challenge, that consumes enormous resources and time. Therefore, the automatic separation of the lithologies within rocks of reservoirs has attracted the increasing attention of many research groups. The consistent classification of these lithologies is the main factor for the construction of reliable depositional, diagenetic, and reservoir models. This work deals with this last issue by presenting the development of a technique for the automatic classification of carbonate thin sections obtained from plane-polarized and cross-polarized microscopy images corresponding to natural rocks belonging to the Brazilian pre-salt reservoir. Our proposed model transforms the analyzed images into structured data by defining texture parameters (Haralick parameters), and Wavelets transforms. Later, a stacked autoencoder neural network is used to eliminate images with anomalies and/or distortions in order to define relevant characteristics of the data. This stage is followed by supervised classifier called multilayer feed-forward neural network. The definition of the model’s hyperparameters is tuned by Bayesian optimization and the Gaussian process. For training and testing of the network, images of 570 thin sections were used (each image obtained with plane-polarized and cross-polarized light) totaling 1140 images. Our model reported an accuracy of 83% for the test samples, confirming the validity of the proposed model in the automatic classification of carbonate rocks.
      PubDate: 2022-10-05
       
  • Automatic reconstruction method of 3D geological models based on deep
           convolutional generative adversarial networks

    • Free pre-print version: Loading...

      Abstract: Abstract How to reconstruct a credible three-dimensional (3D) geological model from very limited survey data, e.g. boreholes, outcrop, and two-dimensional (2D) images, is challenging in the field of 3D geological modeling. Against the limitations of the huge computational consumption and complex parameterization of geostatistics-based stochastic simulation methods, we propose an automatic reconstruction method of 3D geological models based on deep convolutional generative adversarial network (DCGAN). In this work, 2D geological sections are used as conditioning data to generate 3D geological models automatically. Various realizations can be reproduced under a same DCGAN model established through deep network training. A U-Net structure is used to enhance the fitting effect of the DCGAN model. In addition, joint loss functions are exploited to increase the similarity between 3D realizations and reference models. Three synthetic datasets were used to verify the capability of the method presented in this paper. Experimental results show that the proposed 3D automatic reconstruction method based on DCGAN can capture the features, trends and spatial patterns of geological structures well. The output models obey the used conditioning data. The complex heterogeneous structures are reconstructed more accurately and quickly by using the proposed method.
      PubDate: 2022-10-01
       
  • Model-based characterization of permeability damage control through
           inhibitor injection under parametric uncertainty

    • Free pre-print version: Loading...

      Abstract: Abstract Damage in subsurface formations caused by mineral precipitation decreases the porosity and permeability, eventually reducing the production rate of wells in plants producing oil, gas or geothermal fluids. A possible solution to this problem consists in stopping the production followed by the injection of inhibiting species that slow down the precipitation process. In this work we model inhibitor injection and quantify the impact of a set of model parameters on the outputs of the system. The parameters investigated concern three key factors contributing to the success of the treatment: i) the inhibitor affinity, described by an adsorption Langmuir isotherm, ii) the concentration and time related to the injection and iii) the efficiency of the inhibitor in preventing mineral precipitation. Our simulations are set in a stochastic framework where these inputs are characterized in probabilistic terms. Forward simulations rely on a purpose-built code based on finite differences approximation of the reactive transport setup in radial coordinates. We explore the sensitivity diverse outputs, encompassing the well bottom pressure and space-time scales characterizing the transport of the inhibitor. We find that practically relevant output variables, such as inhibitor lifetime and well bottom pressure, display a diverse response to input uncertainties and display poor mutual dependence. Our results quantify the probability of treatment failure for diverse scenarios of inhibitor-rock affinity. We find that treatment optimization based on single outputs may lead to high failure probability when evaluated in a multi-objective framework. For instance, employing an inhibitor displaying an appropriate lifetime may fail in satisfying criteria set in terms of well-bottom pressure history or injected inhibitor mass.
      PubDate: 2022-10-01
       
  • On several numerical strategies to solve Richards’ equation in
           heterogeneous media with finite volumes

    • Free pre-print version: Loading...

      Abstract: Abstract We propose several numerical approaches building on upstream mobility two-point flux approximation finite volumes to solve Richards’ equation in domains made of several rock-types. Our study encompasses four different schemes corresponding to different ways to approximate the nonlinear transmission condition systems arising at the interface between different rocks consisting in the continuity of the pressure and the fluxes, as well as different resolution strategies based on Newton’s method with variable switch. We benchmark the robustness and accuracy of the different methods on filling and drainage test cases with standard nonlinearities of Brooks-Corey and van Genuchten-Mualem type, as well as with challenging steep nonlinearities.
      PubDate: 2022-10-01
       
  • Investigation of thermal-hydro-mechanical coupled fracture propagation
           considering rock damage

    • Free pre-print version: Loading...

      Abstract: Abstract Thermal-hydro-mechanical (THM) coupled fracture propagation is common in underground engineering. Rock damage, as an inherent property of rock, significantly affects fracture propagation, but how it influences the THM coupled fracturing remains stubbornly unclear. A pore-scale THM coupling model is developed to study this problem, which combines the lattice Boltzmann method (LBM), the discrete element method (DEM), and rock damage development theory together for the first time. This model can more accurately calculate the exchanged THM information at the fluid-solid boundary and fluid conductivity dependent on fracture and rock damage. Based on the developed model, the synergistic effect of injected temperature difference (fluid temperature below rock temperature) and rock damage (characterized by the parameter “critical fracture energy”, abbreviated as “CFE”) on fracture propagation of shale are investigated particularly. It is found that: (1) the generation of branched cracks is closely related to the temperature response frontier, and the fracture process zone of single bond failure increases in higher CFE. (2) through the analysis of micro failure events, hydraulic fracturing is more pronounced in the low CFE, while thermal fracturing displays the opposite trend. The fluid conductivity of fractured rock increases with a higher injected temperature difference due to the more penetrated cracks and wider fracture aperture. However, this enhancement weakens when rock damage is significant. (3) in the multiple-layered rock with various CFEs, branched cracks propagating to adjacent layers are more difficult to form when the injection hole stays in the layer with significant rock damage than without rock damage.
      PubDate: 2022-10-01
       
  • Use of low-fidelity models with machine-learning error correction for well
           placement optimization

    • Free pre-print version: Loading...

      Abstract: Abstract Well placement optimization is commonly performed using population-based global stochastic search algorithms. These optimizations are computationally expensive due to the large number of multiphase flow simulations that must be conducted. In this work, we present an optimization framework in which these simulations are performed with low-fidelity (LF) models. These LF models are constructed from the underlying high-fidelity (HF) geomodel using a global transmissibility upscaling procedure. Tree-based machine-learning methods, specifically random forest and light gradient boosting machine, are applied to estimate the error in objective function value (in this case net present value, NPV) associated with the LF models. In the offline (preprocessing) step, preliminary optimizations are performed using LF models, and a clustering procedure is applied to select a representative set of 100–150 well configurations to use for training. HF simulation is then performed for these configurations, and the tree-based models are trained using an appropriate set of features. In the online (runtime) step, optimization with LF models, with the machine-learning correction, is conducted. Differential evolution is used for all optimizations. Results are presented for two example cases involving the placement of vertical wells in 3D bimodal channelized geomodels. We compare the performance of our procedure to optimization using HF models. In the first case, 25 optimization runs are performed with both approaches. Our method provides an overall speedup factor of 46 relative to optimization using HF models, with the best-case NPV within 1% of the HF result. In the second case fewer HF optimization runs are conducted (consistent with actual practice), and the overall speedup factor with our approach is about 8. In this case, the best-case NPV from our procedure exceeds the HF result by 3.8%.
      PubDate: 2022-10-01
       
  • Upscaling of two-phase discrete fracture simulations using a convolutional
           neural network

    • Free pre-print version: Loading...

      Abstract: Abstract Upscaling methods such as the dual porosity/dual permeability (DPDP) model provide a robust means for numerical simulation of fractured reservoirs. In order to close the DPDP model, one needs to provide the upscaled fracture permeabilities and the parameters of the matrix-fracture mass transfer for every fractured coarse block in the domain. Obtaining these model closures from fine-scale discrete fracture-matrix (DFM) simulations is a lengthy and computationally expensive process. We alleviate these difficulties by pixelating the fracture geometries and predicting the upscaled parameters using a convolutional neural network (CNN), trained on precomputed fine-scale results. We demonstrate that once a trained CNN is available, it can provide the DPDP model closures for a wide range of modeling parameters, not only those for which the training dataset has been obtained. The performance of the DPDP model with both reference and predicted closures is compared to the reference DFM simulations of two-phase flows using a synthetic and a realistic fracture geometries. While the both DPDP solutions underestimate the matrix-fracture transfer rate, they agree well with each other and demonstrate a significant speedup as compared to the reference fine-scale solution.
      PubDate: 2022-10-01
       
  • An intelligent multi-fidelity surrogate-assisted multi-objective reservoir
           production optimization method based on transfer stacking

    • Free pre-print version: Loading...

      Abstract: Abstract Recently, many researchers have focused on reservoir production optimization because it is one of the most essential processes in closed-loop reservoir management. Surrogate-assisted production optimization in particular has received a lot of research attention. This technique applies a simple yet vigorous approximation model to substitute expensive numerical simulation runs. However, almost all the existing methods independently use a single approximation model and neglect the potential synergies between these models. In order to make full use of the potential synergies of these existing approximation models, a novel multi-fidelity (MF) surrogate-assisted multi-objective production optimization (MOPO) method based on transfer stacking (MFTS-MOPO) is proposed. In the MFTS-MOPO method, the radial basis function network and support vector regression surrogate models are applied to approximate the high-fidelity (HF) model as the two additional low-fidelity (LF) models. Then a multi-fidelity surrogate model is adopted to evaluate objectives during the optimization process by transferring the two additional and streamline low-fidelity models to the computationally expensive high-fidelity model. Furthermore, two sampling infill strategies are applied to efficiently improve the quality of the multi-fidelity surrogate model. The uniqueness of the proposed MFTS-MOPO method is that the transfer stacking technique is employed to efficiently use the information from different fidelity models to establish the MF surrogate model and the infill sampling strategy used to improve its performance. In addition, three benchmark problems and two reservoirs with different scales were applied to illustrate the effectiveness and accuracy of the MFTS-MOPO method. It was found that the MFTS-MOPO method had superior performance in convergence and diversity than other conventional methods.
      PubDate: 2022-10-01
       
  • Application of machine learning to characterize gas hydrate reservoirs in
           Mackenzie Delta (Canada) and on the Alaska north slope (USA)

    • Free pre-print version: Loading...

      Abstract: Abstract Artificial neural network-trained models were used to predict gas hydrate saturation distributions in permafrost-associated deposits in the Eileen Gas Hydrate Trend on the Alaska North Slope (ANS), USA and at the Mallik research site in the Beaufort-Mackenzie Basin, Northwest Territories, Canada. The database of Logging-While-Drilling (LWD) and wireline logs collected at five wells (Mount Elbert, Iġnik Sikumi, and Kuparuk 7–11–12 wells at ANS, plus 2L-38 and 5L-38 wells at the Mallik research site) includes more than 10,000 depth points, which were used for training, validation, and testing the machine learning (ML) models. Data used in training the ML models include the well logs of density, porosity, electrical resistivity, gamma radiation, and acoustic wave velocity measurements. Combinations of two or three out of these five well logs were found to reliably predict the gas hydrate saturation with accuracy varying between 80 and 90% when compared to the gas hydrate saturations derived from Nuclear Magnetic Resonance (NMR)-based technique. The ML models trained on data from three ANS wells achieved high fidelity predictions of gas hydrate saturation at the Mallik site. The results obtained in this study indicate that ML models trained on data from one geological basin can successfully predict key reservoir parameters for permafrost-associated gas hydrate accumulations within another basin. A generalized approach for selecting a well log combination that can improve model accuracy is discussed. Overall, the study outcome supports earlier work demonstrating that ML models trained on non-NMR well logs are a viable alternative to physics-driven methods for predicting gas hydrate saturations.
      PubDate: 2022-10-01
       
  • 3D reconstruction of porous media using a batch normalized variational
           auto-encoder

    • Free pre-print version: Loading...

      Abstract: Abstract The 3D reconstruction of porous media plays a key role in many engineering applications. There are two main methods for the reconstruction of porous media: physical experimental methods and numerical reconstruction methods. The former are usually expensive and restricted by the limited size of experimental samples, while the latter are relatively cost-effective but still suffer from a lengthy processing time and unsatisfactory performance. With the vigorous development of deep learning in recent years, applying deep learning methods to 3D reconstruction of porous media has become an important direction. Variational auto-encoder (VAE) is one of the typical deep learning methods with a strong ability of extracting features from training images (TIs), but it has the problem of posterior collapse, meaning the generated data from the decoder are not related to its input data, i.e. the latent space Z. This paper proposes a VAE model (called SE-FBN-VAE) based on squeeze-and-excitation network (SENet) and fixed batch normalization (FBN) for the reconstruction of porous media. SENet is a simple and efficient channel attention mechanism, which improves the sensitivity of the model to channel characteristics. The application of SENet to VAE can further improve its ability of extracting features from TIs. Batch normalization (BN) is a common method for data normalization in neural networks, which reduces the convergence time of the network. In this paper, BN is slightly modified to solve the problem of posterior collapse of VAE. Compared with some other numerical methods, the effectiveness and practicability of the proposed method are demonstrated.
      PubDate: 2022-10-01
       
  • Sequential design strategy for kriging and cokriging-based machine
           learning in the context of reservoir history-matching

    • Free pre-print version: Loading...

      Abstract: Abstract Numerical models representing geological reservoirs can be used to forecast production and help engineers to design optimal development plans. These models should be as representative as possible of the true dynamic behavior and reproduce available static and dynamic data. However, identifying models constrained to production data can be very challenging and time consuming. Machine learning techniques can be considered to mimic and replace the fluid flow simulator in the process. However, the benefit of these approaches strongly depends on the simulation time required to train reliable predictors. Previous studies highlighted the potential of the multi-fidelity approach rooted in cokriging to efficiently provide accurate estimations of fluid flow simulator outputs. This technique consists in combining simulation results obtained on several levels of resolution for the reservoir model to predict the output properties on the finest level (the most accurate one). The degraded levels can correspond for instance to a coarser discretization in space or time, or to less complex physics. The idea behind is to take advantage of the coarse level low-cost information to limit the total simulation time required to train the meta-models. In this paper, we propose a new sequential design strategy for iteratively and automatically training (kriging and) cokriging based meta-models. As highlighted on two synthetic cases, this approach makes it possible to identify training sets leading to accurate estimations for the error between measured and simulated production data (objective function) while requiring limited simulation times.
      PubDate: 2022-10-01
       
  • Conditioning geological surfaces to horizontal wells

    • Free pre-print version: Loading...

      Abstract: Abstract Kriging is a standard method for conditioning surfaces to observations. Kriging works for vertical wells, but may produce surfaces that cross horizontal wells between surface observations. We establish an approach that also works for horizontal wells, where surfaces are modeled as a set of correlated Gaussian random fields. The constraints imposed by the horizontal wells makes the conditional surfaces non-Gaussian. We present a method for exact conditional simulation and an approximation for prediction and prediction uncertainty. Thousands of constraints can be handled efficiently without numerical instabilities. The approach is illustrated with synthetic and real examples that show how the constraints influence the surfaces and reduce uncertainty.
      PubDate: 2022-10-01
       
  • Gas-oil gravity drainage mechanism in fractured oil reservoirs: surrogate
           model development and sensitivity analysis

    • Free pre-print version: Loading...

      Abstract: Abstract In this study, a surrogate model is developed based on single porosity modelling approach to predict the gas-oil gravity drainage recovery curves. The single porosity model is validated against experimental data associated with gravity drainage available for an actual core sample gathered from a naturally fractured reservoir. Then, using the single porosity model and a vast databank of rock and fluid data, the gravity drainage recovery curves are generated. Two empirical functions, namely Lambert and Aronofsky are then utilized to match the generated recovery curves. An exact graphical approach based on saturation profiles is put forth to compute ultimate oil recovery and an artificial neural network is developed to predict the convergence constant. The findings imply that unlike Lambert, Aronofsky is more accurate in capturing the simulation-generated recovery curves. The Lambert function overpredicts the early-time and underpredicts late-time oil recovery in most cases. The statistical parameters and plotted graphs indicate that the developed surrogate model is robust and precise in predicting the Aronofsky function convergence constant. In addition, it is found that unlike absolute permeability and maximum oil relative permeability, other parameters including block height, oil viscosity, and porosity directly correlate with the time required to reach ultimate oil recovery. The presented graphical approach indicates that initial water saturation, density difference, block height, and capillary pressure curve are the only parameters that could affect the ultimate oil recovery. The sensitivity analysis aided by the developed surrogate model and Monte Carlo algorithm affirms that absolute permeability and density difference are the parameters that have the maximum and minimum impact, respectively, on the time required to reach the ultimate recovery.
      PubDate: 2022-10-01
       
  • Determination of Two-phase Relative Permeability from a Displacement with
           Safman-Rayleigh Instability Using a Coarse-Scale Model History Matching
           Approach

    • Free pre-print version: Loading...

      Abstract: Abstract In heavy oil displacement by fluid injection, severe instability can occur due to the adverse mobility ratio, gravity segregation or compositional effects. However, when estimating relative permeability, most analytical methods assume a stable front in the displacement, which may be highly erroneous when used with unstable displacements. Quite often in such cases, history matching using a high-resolution model is preferred, however, it is computationally inefficient or impractical in some cases. This work describes a relatively fast methodology for estimating relative permeability from displacement with instability and compositional effect. It involves defining a set of 2-dimensional (2D) coarse-grid models and the tuning of a three-parameter correlation. By this approach, an attempt is made to resolve the fine-scale information without direct solution of the global fine-scale problem. The results of the coarse-grid history matching corrected by the proposed approach in this study showed that the improved methodology is three times faster, and required less than half the memory of a high-resolution 2D model.
      PubDate: 2022-10-01
       
  • Coastline matching via a graph-based approach

    • Free pre-print version: Loading...

      Abstract: Abstract This paper studies the problem of unsupervised detection of geometrically similar fragments (segments) in curves, in the context of boundary matching. The goal is to determine all pairs of sub-curves that are geometrically similar, under local scale invariance. In particular, we aim to locate the existence of a similar section (independent of length and/or orientation) in the second curve, to a section of the first curve, as indicated by the user. The proposed approach is based on a suitable distance matrix of the two given curves. Additionally, a suitable objective function is proposed to capture the trade-off between the similarity of the common sub-sequences and their lengths. The goal of the algorithm is to minimize this objective function via an efficient graph-based approach that capitalizes on Dynamic Time Warping to compare the two subcurves. We apply the proposed technique in the context of geometric matching of coastline pairs. This application is crucial for investigating the forcing factors related to the coastline evolution. The proposed method was successfully applied to global coastline data, yielding a bipartite graph with analytical point-to-point correspondences.
      PubDate: 2022-09-30
       
  • Design of optimal operational parameters for steam-alternating-solvent
           processes in heterogeneous reservoirs – A multi-objective optimization
           approach

    • Free pre-print version: Loading...

      Abstract: Abstract Steam injection is a widely-used process for heavy oil and bitumen recovery. However, a significant drawback is the excessive energy requirement, water consumption, and CO2 generation. The Steam Alternating Solvent (SAS) process has been proposed as an eco-friendlier alternative to the existing steam-based methods. It involves injecting steam and solvent (i.e. propane) in separate cycles. The interplay between reservoir heterogeneity and the complex physical mechanisms of heat and mass transfer has made optimizing its design parameters quite challenging. This work aims to develop a hybrid Multi-Objective Optimization (MOO) scheme for determining the optimal operational parameters using the Pareto dominance concept while considering several conflicting objectives (i.e. RF, steam, and solvent injection) under several heterogeneous scenarios. A 2-D base homogenous reservoir model is built according to the Fort McMurray formation in the Athabasca region in Alberta, Canada. Next, shale barriers with varying proportions, lengths, and locations are superimposed onto the base model. Then, a sensitivity analysis is performed to assess the controllable operational parameters’ impact and formulate several objective functions; proxy models are introduced to speed up the objective function evaluations. Finally, different Multi-Objective Evolutionary Algorithms (MOEAs) are applied to establish the optimal ranges to operate the selected decision variables. Different optimal operating strategies are needed depending on the shale barrier distribution. Injector bottom-hole pressure, steam trap, and producer gas rate significantly impact the model response. Injecting high propane concentration over short durations is recommended. The length of the steam injection phase seems to be more sensitive to the reservoir heterogeneities; extended steam injection is needed for the more heterogeneous models. This paper is the first work comparing different MOEAs to optimize the SAS process using multiple heterogeneous models.
      PubDate: 2022-09-29
       
  • Numerical simulation of crack propagation and coalescence in rock
           

    • Free pre-print version: Loading...

      Abstract: Abstract The strain energy density softening criterion is introduced to the peridynamic theory to reflect the failure characteristics of rock-like materials. At the same time, the critical damage condition, obeying Weibull distribution, is utilized to describe the heterogeneity of rock—making up for the deficiency that the peridynamic method cannot embody the strain-softening features and heterogeneity of rock when simulating crack propagation of rock materials. The crack extension process of rock, with a single crack under uniaxial compression, was simulated using the improved method, the results of which were then compared to the results of previous laboratory tests to verify the effectiveness of the proposed theory. Furthermore, the crack propagation patterns and failure mechanism of rock materials which contain single crack and double cracks with different prefabrication angles under uniaxial compression condition were explored in this study. The results showed that the propagation of a prefabricated single crack specimen was divided into wing crack, shear crack, and anti-wing crack, and the order of appearance was influenced by the inclination angle. The penetration modes of prefabricated double cracks were divided into wing mode (W-mode), shear mode (S-mode), and mixed mode (M-mode), which were primarily affected by the inclination angle and relative angle.
      PubDate: 2022-09-26
       
  • The traveling wavefront for foam flow in two-layer porous media

    • Free pre-print version: Loading...

      Abstract: Abstract The injection of foams into porous media has gained importance as a method of controlling gas mobility. The multilayer structure of the porous medium raises a question on its efficiency in dealing with layers of different permeabilities. The present work shows the existence of a single traveling wavefront in a two-layer porous medium for a simplified model, which was derived from a realistic two-dimensional one. Besides the necessary conditions for the solution’s existence, we prove that the traveling wave velocity is a weighted average of the velocities as if both layers were isolated. All theoretical estimates were validated through one- and two-dimensional simulations. Finally, we estimated the order of magnitude of the characteristic time the traveling wavefront needs to stabilize.
      PubDate: 2022-09-24
       
  • An approximate cut-cell discretization technique for flow in fractured
           porous media

    • Free pre-print version: Loading...

      Abstract: Abstract A new discretization technique for fractured porous media is presented. The most accurate representation for such system is the discrete fracture and matrix (DFM) model where the fractures, their intersections, and the surrounding rock are explicitly represented using a conforming mesh. However, the construction of such meshes becomes challenging for complex systems. The objective of the proposed method is to construct an equivalent DFM model without explicitly constructing a conforming mesh. The method is based on an approximate cut-cell framework where all geometrical quantities needed for discretization are estimated numerically using a local subgrid. The method has built in simplification capabilities and is not very sensitive to the complexity of the model. It is able to generate an equivalent DFM model as well as an embedded discrete fracture model (EDFM). The methodology will be described in detail and illustrated with examples of varying degrees of complexity.
      PubDate: 2022-09-24
       
  • Evaluating the impact of increasing temperatures on changes in Soil
           Organic Carbon stocks: sensitivity analysis and non-standard discrete
           approximation

    • Free pre-print version: Loading...

      Abstract: Abstract The SOC change index, defined as the normalized difference between the actual Soil Organic Carbon and the value assumed at an initial reference year, is here tailored to the RothC carbon model dynamics. It assumes as a baseline the value of the SOC equilibrium under constant environmental conditions. A sensitivity analysis is performed to evaluate the response of the model to changes in temperature, Net Primary Production (NPP), and land use soil class (forest, grassland, arable). A non-standard monthly time-stepping procedure has been proposed to approximate the SOC change index in the Alta Murgia National Park, a protected area in the Italian Apulia region, selected as a test site. The SOC change index exhibits negative trends for all the land use considered without fertilizers. The negative trend in the arable class can be inverted by a suitable organic fertilization program here proposed.
      PubDate: 2022-08-03
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 3.223.3.251
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-