The Impact of Completion and Stimulation Activities on Gas Production in Marcellus Shale: Analytics in a Big Data Environment

Author(s):  
Wen-ting Yue ◽  
Yi Wang ◽  
Yu-wen Chang ◽  
Zuo-qian Wang
2021 ◽  
Author(s):  
Mohamed El Sgher ◽  
Kashy Aminian ◽  
Ameri Samuel

Abstract The objective of this study was to investigate the impact of the hydraulic fracturing treatment design, including cluster spacing and fracturing fluid volume on the hydraulic fracture properties and consequently, the productivity of a horizontal Marcellus Shale well with multi-stage fractures. The availability of a significant amount of advanced technical information from the Marcellus Shale Energy and Environment Laboratory (MSEEL) provided an opportunity to perform an integrated analysis to gain valuable insight into optimizing fracturing treatment and the gas recovery from Marcellus shale. The available technical information from a horizontal well at MSEEL includes well logs, image logs (both vertical and lateral), diagnostic fracture injection test (DFIT), fracturing treatment data, microseismic recording during the fracturing treatment, production logging data, and production data. The analysis of core data, image logs, and DFIT provided the necessary data for accurate prediction of the hydraulic fracture properties and confirmed the presence and distribution of natural fractures (fissures) in the formation. Furthermore, the results of the microseismic interpretation were utilized to adjust the stress conditions in the adjacent layers. The predicted hydraulic fracture properties were then imported into a reservoir simulation model, developed based on the Marcellus Shale properties, to predict the production performance of the well. Marcellus Shale properties, including porosity, permeability, adsorption characteristics, were obtained from the measurements on the core plugs and the well log data. The Quanta Geo borehole image log from the lateral section of the well was utilized to estimate the fissure distribution s in the shale. The measured and published data were utilized to develop the geomechnical factors to account for the hydraulic fracture conductivity and the formation (matrix and fissure) permeability impairments caused by the reservoir pressure depletion during the production. Stress shadowing and the geomechanical factors were found to play major roles in production performance. Their inclusion in the reservoir model provided a close agreement with the actual production performance of the well. The impact of stress shadowing is significant for Marcellus shale because of the low in-situ stress contrast between the pay zone and the adjacent zones. Stress shadowing appears to have a significant impact on hydraulic fracture properties and as result on the production during the early stages. The geomechanical factors, caused by the net stress changes have a more significant impact on the production during later stages. The cumulative gas production was found to increase as the cluster spacing was decreased (larger number of clusters). At the same time, the stress shadowing caused by the closer cluster spacing resulted in a lower fracture conductivity which in turn diminished the increase in gas production. However, the total fracture volume has more of an impact than the fracture conductivity on gas recovery. The analysis provided valuable insight for optimizing the cluster spacing and the gas recovery from Marcellus shale.


Author(s):  
Georgina Nugent-Folan ◽  
Jennifer Edmond

One of the major terminological forces driving information and communication technology (ICT) integration in research today is “big data.” The characteristics of big data make big data sound inclusive and integrative. However, in practice such approaches are highly selective, excluding input that cannot be effectively structured, represented, or digitized; in other words, excluding complex data. Yet complex data are precisely the kind that human activity tends to produce, but the technological imperative to enhance signal through the reduction of noise does not accommodate this richness. The objective of this chapter is to explore the impact of bias in digital approaches to knowledge creation by investigating the delimiting effect digital mediation and datafication can have on rich, complex cultural data. If rich or complex data prove difficult to fully represent on a small-scale level, in the transition to a big data environment, we run the risk of losing much of what makes this material useful or interesting in the first place. We will begin by reviewing some of the existing implicit definitions of data that underlie ICT-driven research. In doing so will draw attention to the heterogeneity of definitions of data, to identify the key terms associated with data demarcation and data use, and to then expand on the implications of this heterogeneity.


2014 ◽  
Vol 631-632 ◽  
pp. 1067-1070
Author(s):  
Yang Li

The advent of big data era brings new development and opportunities for the construction of library information resources. This paper analyzes the impact of big data on the library at home and abroad, and points out the necessary new reform of construction of library information resources in colleges and universities from the perspective of information resource configuration. Under the big data environment, library should develop the data analysis service of big data information resources, establish the literature resources allocation scheme with circulation data as the assessment indicator and develop towards intelligent library.


2017 ◽  
Vol 39 (5) ◽  
pp. 177-202
Author(s):  
Hyun-Cheol Choi
Keyword(s):  
Big Data ◽  

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