scholarly journals Reservoir Flow Unit Analysis of Akos Field in Niger Delta

2020 ◽  
pp. 32-45
Author(s):  
G. O. Emujakporue ◽  
E. E. Enyenihi
Keyword(s):  
Niger Delta ◽  
Storage Capacity ◽  
Well Logs ◽  
Sampling Depth ◽  
Flow Units ◽  
Flow Capacity ◽  
Reservoir Flow ◽  
Porosity And Permeability ◽  
And Storage ◽  
Cumulative Flow

In this study, the flow units of reservoirs of Akos field have been computed with the Stratigraphic Modified Lorenz plot. Cumulative flow capacity and cumulative storage capacity were used for constructing the Stratigraphic Modified Lorenz Plot (SMLP). The flow capacity and storage capacity are functions of calculated permeability and porosity values considering their sampling depth. The porosity and permeability were obtained from composite well logs of eight oil wells in the study area. Two reservoirs A and B were delineated from the well logs. The stratigraphic Modified Lorenz Plots (SMLP) revealed a total of one hundred and twelve (112) Flow units (FU) in the two observed reservoirs A and B. Reservoir A has a total of 53 FU (25 speed zones, 18 baffle zones and 10 barrier zones) and reservoir B has a total of 59 FU (29 speed zones, 16 baffle zones and 14 barrier zones) which cut across all the wells. The flow units in both reservoirs fall within the speed zones, baffles and barrier unit categories. The speed zone units with equal flow and storage capacities are the dominant flow units in both reservoirs. This is an indication that the sediments have good reservoir qualities. The baffle zones have more storage capacity than the speed zones. The barrier zones within the reservoirs are acting as a seal to the flow of fluid.

scholarly journals Transparent CoAP Services to IoT Endpoints through ICN Operator Networks

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1339 ◽  
Author(s):  
Hasan Islam ◽  
Dmitrij Lagutin ◽  
Antti Ylä-Jääski ◽  
Nikos Fotiou ◽  
Andrei Gurtov
Keyword(s):  
Storage Capacity ◽  
Communication Overhead ◽  
Full Potential ◽  
Core Network ◽  
Computation Complexity ◽  
State Management ◽  
Iot Devices ◽  
Novel Applications ◽  
And Storage ◽  
The Internet Of Things

The Constrained Application Protocol (CoAP) is a specialized web transfer protocol which is intended to be used for constrained networks and devices. CoAP and its extensions (e.g., CoAP observe and group communication) provide the potential for developing novel applications in the Internet-of-Things (IoT). However, a full-fledged CoAP-based application may require significant computing capability, power, and storage capacity in IoT devices. To address these challenges, we present the design, implementation, and experimentation with the CoAP handler which provides transparent CoAP services through the ICN core network. In addition, we demonstrate how the CoAP traffic over an ICN network can unleash the full potential of the CoAP, shifting both overhead and complexity from the (constrained) endpoints to the ICN network. The experiments prove that the CoAP Handler helps to decrease the required computation complexity, communication overhead, and state management of the CoAP server.

scholarly journals Transport and storage of serotonin by thrombin-treated platelets.

1975 ◽  
Vol 65 (2) ◽  
pp. 359-372 ◽  
Author(s):  
H J Reimers ◽  
D J Allen ◽  
I A Feuerstein ◽  
J F Mustard
Keyword(s):  
Storage Capacity ◽  
Platelet Membrane ◽  
Transport Rate ◽  
Primary Reason ◽  
High Concentration ◽  
Serotonin Content ◽  
Amine Storage ◽  
Storage Granules ◽  
And Storage ◽  
Amine Storage Granules

Repeated thrombin treatment of washed platelets prepared from rabbits can decrease the serotonin content of the platelets by about 80%. When these platelets are deaggregated they reaccumulate serotonin but their storage capacity for serotonin is reduced by about 60%. If thrombin-pretreated platelets are allowed to equilibrate with a high concentration of serotonin (123 mu M), they release a smaller percentage of their total serotonin upon further thrombin treatment, in comparison with the percentage of serotonin released from control platelets equilibrated with the same concentration of serotonin calculations indicate that in thrombin-treated platelets reequilibrated with serotonin, two-thirds of the serotonin is in the granule compartment and one-third is in the extragranular compartment, presumably the cytoplasm. Analysis of the exchange of serotonin between the suspending fluid and the platelets showed that thrombin treatment does not alter the transport rate of serotonin across the platelet membrane and does not cause increased diffusion of serotonin from the platelets into the suspending fluid. The primary reason for the reduced serotonin accumulation by the thrombin-treated platelets appears to be loss of amine storage granules or of the storage capacity within the granules.

scholarly journals Enhancing Associative Memory Recall and Storage Capacity Using Confocal Cavity QED

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Brendan P. Marsh ◽  
Yudan Guo ◽  
Ronen M. Kroeze ◽  
Sarang Gopalakrishnan ◽  
Surya Ganguli ◽  
...  
Keyword(s):  
Associative Memory ◽  
Cavity Qed ◽  
Storage Capacity ◽  
Memory Recall ◽  
Confocal Cavity ◽  
And Storage

Machine Learning for Multiple Petrophysical Properties Regression Based on Core Images and Well Logs in a Heterogenous Reservoir

2021 ◽  
Author(s):  
Tao Lin ◽  
Mokhles Mezghani ◽  
Chicheng Xu ◽  
Weichang Li
Keyword(s):  
Machine Learning ◽  
Model Building ◽  
Image Features ◽  
Well Logs ◽  
Model Complexity ◽  
Rock Properties ◽  
Core Analysis ◽  
Petrophysical Properties ◽  
Blind Test ◽  
Porosity And Permeability

Abstract Reservoir characterization requires accurate prediction of multiple petrophysical properties such as bulk density (or acoustic impedance), porosity, and permeability. However, it remains a big challenge in heterogeneous reservoirs due to significant diagenetic impacts including dissolution, dolomitization, cementation, and fracturing. Most well logs lack the resolution to obtain rock properties in detail in a heterogenous formation. Therefore, it is pertinent to integrate core images into the prediction workflow. This study presents a new approach to solve the problem of obtaining the high-resolution multiple petrophysical properties, by combining machine learning (ML) algorithms and computer vision (CV) techniques. The methodology can be used to automate the process of core data analysis with a minimum number of plugs, thus reducing human effort and cost and improving accuracy. The workflow consists of conditioning and extracting features from core images, correlating well logs and core analysis with those features to build ML models, and applying the models on new cores for petrophysical properties predictions. The core images are preprocessed and analyzed using color models and texture recognition, to extract image characteristics and core textures. The image features are then aggregated into a profile in depth, resampled and aligned with well logs and core analysis. The ML regression models, including classification and regression trees (CART) and deep neural network (DNN), are trained and validated from the filtered training samples of relevant features and target petrophysical properties. The models are then tested on a blind test dataset to evaluate the prediction performance, to predict target petrophysical properties of grain density, porosity and permeability. The profile of histograms of each target property are computed to analyze the data distribution. The feature vectors are extracted from CV analysis of core images and gamma ray logs. The importance of each feature is generated by CART model to individual target, which may be used to reduce model complexity of future model building. The model performances are evaluated and compared on each target. We achieved reasonably good correlation and accuracy on the models, for example, porosity R2=49.7% and RMSE=2.4 p.u., and logarithmic permeability R2=57.8% and RMSE=0.53. The field case demonstrates that inclusion of core image attributes can improve petrophysical regression in heterogenous reservoirs. It can be extended to a multi-well setting to generate vertical distribution of petrophysical properties which can be integrated into reservoir modeling and characterization. Machine leaning algorithms can help automate the workflow and be flexible to be adjusted to take various inputs for prediction.

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