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Reservoir modeling

About: Reservoir modeling is a(n) research topic. Over the lifetime, 5746 publication(s) have been published within this topic receiving 62195 citation(s).
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Journal ArticleDOI
Abstract: The Middle Pliocene Fasila and Balakhany Suites are important petroleum-bearing reservoirs in the South Caspian Basin. There are four lithofacies associated with the interbedded sandprone reservoir intervals. The mineral constituents, sedimentary structures and depositional setting of the productive interval are interpreted as river-dominated depositional setting, including conglomeratic facies relating to a channel floor sub-environment and sandstone facies associated with the upper parts of river channels. The drainage basin of the Volga River is considered as the provenance for these deposits derived from basement rock outcrops to the north, which belongs to the Russian Precambrian semi-oceanic continental platform. Reservoir quality is assessed using the core-based porosity and permeability data in relation to the sedimentary and diagenetic processes influencing each lithofacies. Lithofacies 1 and 2 have good reservoir quality, while lithofacies 3 and 4 have poorer reservoir quality. Reservoir quality is controlled by both depositional setting (grain size and texture) and diagenetic imprints associated mainly with compaction, cementation and neumorphism. The role of clay minerals in the diagenesis of the sandstones with increasing burial depth is revealed by SEM and XRD analysis. Clay minerals progressively alter to illite, smectite and chlorite with increasing burial depth. This alteration leads to the occlusion of pore throats and degrading reservoir permeability.

23 Nov 2021
Abstract: Reservoir characterization of dual porosity system and simulation modeling of naturally fractured reservoir present unique challenges because it different from single porosity reservoir. This reservoir is complicated so its need to characterize because have two medium porous there are matrix and fracture and also have two drive mechanism of interaction matrix and fracture that need to be modeled accurately. This study focused on estimation pressure pDand rate dimensionless qD that generated from simulation model. Where the parameter that influence the characteristic of reservoir such as storage capacity ratio, interporosity flow coefficient and various drainage radius, reD is created to characterizing the behavior of naturally fractured reservoir. The result of this study yield different characteristic of behavior in naturally fractured reservoir.

Journal ArticleDOI
20 Nov 2021-Energy
Abstract: The significant non-uniformity of petrophysical properties and ultra-tight permeability distribution in unconventional reservoirs make them critically complex to investigate. Unconventional reservoirs are usually found in thin layers that expand over hundreds to thousands of miles spatially resulting in huge multidimensional data collection at well locations, which make them numerically challenging for the field development studies and operational analysis. To address such problems, the concept of low-rank tensor decomposition is introduced in this study, to be applied for unconventional reservoir modeling to target issues such as huge dataset management and missing data generation. Low-rank tensor decomposition is a powerful tool that can model a wide range of heterogeneous and multidimensional data. It works by extracting the most useful latent information out of several multidimensional data tensors and reconstruct the entire dataset in a compressed format using low-rank tensors. It is a novel idea to be applied in petroleum reservoir engineering to optimize reservoir models as well as field development strategies to significantly improve recovery efficiency and minimize uncertainties.

Journal ArticleDOI
18 Nov 2021-Energies
Abstract: The test study area is the Miocene reservoir of Nam Con Son Basin, offshore Vietnam. In the study we used unsupervised learning to automatically cluster hydraulic flow units (HU) based on flow zone indicators (FZI) in a core plug dataset. Then we applied supervised learning to predict HU by combining core and well log data. We tested several machine learning algorithms. In the first phase, we derived hydraulic flow unit clustering of porosity and permeability of core data using unsupervised machine learning methods such as Ward’s, K mean, Self-Organize Map (SOM) and Fuzzy C mean (FCM). Then we applied supervised machine learning methods including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Boosted Tree (BT) and Random Forest (RF). We combined both core and log data to predict HU logs for the full well section of the wells without core data. We used four wells with six logs (GR, DT, NPHI, LLD, LSS and RHOB) and 578 cores from the Miocene reservoir to train, validate and test the data. Our goal was to show that the correct combination of cores and well logs data would provide reservoir engineers with a tool for HU classification and estimation of permeability in a continuous geological profile. Our research showed that machine learning effectively boosts the prediction of permeability, reduces uncertainty in reservoir modeling, and improves project economics.

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No. of papers in the topic in previous years