Eagle Ford Fluid Type Variation and Completion Optimization: A Case for Data Analytics

2019 ◽  
Author(s):  
Fahd Siddiqui ◽  
Ali Rezaei ◽  
Birol Dindoruk ◽  
Mohamed Y. Soliman
Keyword(s):  
Data Analytics ◽  
Fluid Type ◽  
Eagle Ford

Impact of Reservoir Fluid and Injection Gas on Shales Huff-N-Puff Performance in the Presence of Diffusion, Sorption, and Hysteresis

2021 ◽  
Author(s):  
Khaled Enab ◽  
Hamid Emami-Meybodi
Keyword(s):  
Relative Permeability ◽  
Oil Recovery ◽  
Volatile Oil ◽  
Fluid Type ◽  
Retention Capacity ◽  
Eagle Ford ◽  
Reservoir Fluid ◽  
Black Oil ◽  
The Impact ◽  
And Diffusion

Abstract We assess the huff-n-puff performance in ultratight reservoirs (shales) by conducting large-scale numerical simulations for a wide range of reservoir fluid types (retrograde condensate, volatile oil, black oil) and different injection gases (CO2, C2H6, C3H8) by considering relative permeability hysteresis, diffusion, and sorption. A dual-porosity naturally fractured numerical compositional model is used that considers molecular diffusion and sorption to represent the flow mechanisms during the injection process. Killough's method, Langmuir's adsorption model, and Sigmund correlation are utilized to incorporate hysteresis, sorption, and diffusion, respectively. To investigate the impact of the fluid type, we consider three fluid types from Eagle Ford shale representing retrograde condensate, volatile oil, and black oil. We conduct a comprehensive evaluation of the impact of diffusion, sorption, and hysteresis on the production performance and retention of each fluid and injection gas. Eagle Ford formation is selected because it is the most actively developed shale, and it contains a wide span of PVT windows from dry gas to black oil. The simulation results show that the huff-n-puff process improves the oil recovery by 4-6% when 10% PV of gas is injected. The huff-n-puff efficiency increases with reducing gas-oil-ratio (GOR) as oil recovery from low (GOR) reservoirs is doubled, while recovery from retrograde condensate increased by 20%. C2H6 provides the highest recovery for the black and volatile oil, and CO2 provides the highest recovery for retrograde condensate fluid type. Diffusion and sorption are essential mechanisms to be considered when modeling gas injection to any fluid type in shales. However, the relative permeability hysteresis effect is not significant. Neglecting diffusion during the huff-n-puff process underestimates the oil recovery and retention capacity. The diffusion effect on the oil density reduction is observed more during the soaking period. The diffusion impact increases with higher GOR reservoirs, while the sorption impact decreases with higher GOR. The retention capacity of the injected gas decreases with higher GOR. The diffusion impact on the retention capacity increases with higher GOR. Hence sorption and diffusion must be considered when modeling the huff-n-puff process in ultratight reservoirs.

Big Data Analytics: The Next Big Thing

2019 ◽  
Vol 54 (5) ◽  
pp. 20
Author(s):  
Dheeraj Kumar Pradhan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

2020 ◽  
Vol 49 (5) ◽  
pp. 11-17
Author(s):  
Thomas Wrona ◽  
Pauline Reinecke
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

Data Analytics on Graphs Part II: Signals on Graphs

2020 ◽  
Vol 13 (2-3) ◽  
pp. 158-331
Author(s):  
Ljubiša Stanković ◽  
Danilo Mandic ◽  
Miloš Daković ◽  
Miloš Brajović ◽  
Bruno Scalzo ◽  
...  
Keyword(s):  
Data Analytics

Summary of the SciTech 2020 Technical Panel on In Situ/In Transit Computational Environments for Visualization and Data Analytics

2021 ◽  
Author(s):  
Earl P. Duque ◽  
Steve M. Legensky ◽  
Brad J. Whitlock ◽  
David H. Rogers ◽  
Andrew C. Bauer ◽  
...  
Keyword(s):  
Data Analytics ◽  
In Transit

Data Analytics to Help Improve Hydraulic Fracture Geometry Prediction Using Support Vector Regression: Case Study

2020 ◽  
Author(s):  
Avinash Wesley ◽  
Bharat Mantha ◽  
Ajay Rajeev ◽  
Aimee Taylor ◽  
Mohit Dholi ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Hydraulic Fracture ◽  
Data Analytics ◽  
Support Vector ◽  
Fracture Geometry
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