Pore Scale Representation of Near Wellbore Damage due to Asphaltene Deposition: Effect of Sand Grain Size and the Presence of Clay in Reservoir Rock

2016 ◽  
Author(s):  
Andreas Prakoso ◽  
Abhishek Punase ◽  
Berna Hascakir
Keyword(s):  
Grain Size ◽  
Reservoir Rock ◽  
Pore Scale ◽  
Asphaltene Deposition ◽  
Deposition Effect ◽  
Sand Grain Size

Indirect Estimation of Clastic Reservoir Rock Grain Size from Wireline Logs Using a Supervised Nearest Neighbor Algorithm: Preliminary Results

2021 ◽  
Author(s):  
Fatai Adesina Anifowose ◽  
Mokhles Mustafa Mezghani ◽  
Saeed Saad Shahrani
Keyword(s):  
Machine Learning ◽  
Grain Size ◽  
Nearest Neighbor ◽  
Reservoir Rock ◽  
Alternative Methods ◽  
Preliminary Results ◽  
Wireline Logs ◽  
Core Descriptions ◽  
Core Description ◽  
Clastic Reservoir

Abstract Reservoir rock textural properties such as grain size are typically estimated by direct visual observation of the physical texture of core samples. Grain size is one of the important inputs to petrophysical characterization, sedimentological facies classification, identification of depositional environments, and saturation models. A continuous log of grain size distribution over targeted reservoir sections is usually required for these applications. Core descriptions are typically not available over an entire targeted reservoir section. Physical core data may also be damaged during retrieval or due to plugging. Alternative methods proposed in literature are not sustainable due to their limitations in terms of input data requirements and inflexibility to apply them in environments with different geological settings. This paper presents the preliminary results of our investigation of a new methodology based on machine learning technology to complement and enhance the traditional core description and the alternative methods. We developed and optimized supervised machine learning models comprising K-nearest neighbor (KNN), support vector machines (SVM), and decision tree (DT) to indirectly estimate reservoir rock grain size for a new well or targeted reservoir sections from historical wireline logs and archival core descriptions. We used anonymized datasets consisting of nine wells from a clastic reservoir. Seven of the wells were used to train and optimize the models while the remaining two were reserved for validation. The grain size types range from clay to pebbles. The performance of the models confirmed the feasibility of this approach. The KNN, SVM, and DT models demonstrated the capability to estimate the grain size for the test wells by matching actual data with a minimum of 60% and close to 80% accuracy. This is an accomplishment taking into account the uncertainties inherent in the core analysis data. Further analysis of the results showed that the KNN model is the most accurate in performance compared to the other models. For future studies, we will explore more advanced classification algorithms and implement new class labeling strategies to improve the accuracy of this methodology. The attainment of this objective will further help to handle the complexity in the grain size estimation challenge and reduce the current turnaround time for core description.

Influence of sand grain size and nest microenvironment on incubation success, hatchling morphology and locomotion performance of green turtles (Chelonia mydas) at the Chagar Hutang Turtle Sanctuary, Redang Island, Malaysia

2018 ◽  
Vol 66 (6) ◽  
pp. 356 ◽  
Author(s):  
Taylor A. Stewart ◽  
David T. Booth ◽  
Mohd Uzair Rusli
Keyword(s):  
Particle Size ◽  
Grain Size ◽  
Oxygen Partial Pressure ◽  
Partial Pressure ◽  
Swimming Speed ◽  
Green Turtle ◽  
Chelonia Mydas ◽  
Sand Particle ◽  
Emergence Success ◽  
Sand Grain Size

The nest microenvironment affects hatching and emergence success, sex ratios, morphology, and locomotion performance of hatchling sea turtles. Sand grain size is hypothesised to influence the nest microenvironment, but the influence of sand grain size on incubation of sea turtle eggs has rarely been experimentally tested. At the Chagar Hutang Turtle Sanctuary, Redang Island, Malaysia, green turtle (Chelonia mydas) nests were relocated to sands with different sand grain sizes on a natural beach to assess whether grain size affects nest temperature, oxygen partial pressure inside the nest, incubation success, hatchling morphology and hatchling locomotion performance. Green turtle nests in coarse sand were cooler; however, hatching success, nest emergence success, oxygen partial pressure, incubation length and hatchling size were not influenced by sand particle size. Nests in medium-grained sands were warmest, and hatchlings from these nests were better self-righters but poorer crawlers and swimmers. Hatchling self-righting ability was not correlated with crawling speed or swimming speed, but crawling speed was correlated with swimming speed, with hatchlings typically swimming 1.5–2 times faster than they crawled. Hence, we found that sand particle size had minimal influence on the nest microenvironment and hatchling outcomes.

Laboratory investigation of dune erosion as a function of sand grain size and dune density

1994 ◽  
Vol 23 (1-2) ◽  
pp. 151-165 ◽  
Author(s):  
M.F. Overton ◽  
W.A. Pratikto ◽  
J.C. Lu ◽  
J.S. Fisher
Keyword(s):  
Grain Size ◽  
Laboratory Investigation ◽  
Dune Erosion ◽  
Sand Grain Size
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