Proppants Selection Based on Field Case Studies of Well Production Performance in the Bakken Shale Play

2014 ◽  
Author(s):  
Kyle Hu ◽  
Jianlei Sun ◽  
Joe Wong ◽  
Bobby E. Hall
Keyword(s):  
Case Studies ◽  
Production Performance ◽  
Field Case ◽  
Well Production ◽  
Bakken Shale

Ancient Connections in Eurasia

2021 ◽  
Author(s):  
Carolin Röding ◽  
Katerina Harvati ◽  
Matteo Scardovelli ◽  
Solange Rigard ◽  
Michela Leonardi ◽  
...  
Keyword(s):  
Case Studies ◽  
Original Research ◽  
Human Populations ◽  
Cultural Traditions ◽  
Field Case ◽  
Cultural Connections

Models pertaining to the antiquity and continuity of Eurasian human populations and their cultural traditions have been revised in recent years as a result of novel inter-disciplinary research. In this third installment of the DFG Center for Advanced Studies Series, experts provide new field case studies, reviews, and original research on bio-cultural connections in Eurasia since the Paleolithic.

A Faster Cleanup, Produced Water-Compatible Fracturing Fluid: Fluid Designs and Field Case Studies

2008 ◽  
Author(s):  
Daren Bulat ◽  
Yiyan Chen ◽  
Matthew Kevin Graham ◽  
Richard Peter Marcinew ◽  
Adegoke S. Adeogun ◽  
...  
Keyword(s):  
Case Studies ◽  
Produced Water ◽  
Fracturing Fluid ◽  
Field Case

Improving Well Production Performance By Using Realtime Wash Desalting Planning Tool WDPT in the North Sea

2020 ◽  
Author(s):  
Ryvo Octaviano ◽  
Erik Hornstra ◽  
Jonah Poort ◽  
Pejman Shoeibi Omrani ◽  
Ruud van der Linden ◽  
...  
Keyword(s):  
North Sea ◽  
Production Performance ◽  
Planning Tool ◽  
Well Production ◽  
The North ◽  
The North Sea

scholarly journals A Review on “Management by Exception” Surveillance for Well Management: To maximize Oil Production

2018 ◽  
Vol 7 (3.25) ◽  
pp. 90
Author(s):  
Azlinda Abdul Malik ◽  
Mohd Hilmi Hasan ◽  
Mazuin Jasamai
Keyword(s):  
Literature Review ◽  
Business Processes ◽  
Oil And Gas ◽  
Production Performance ◽  
Data Driven ◽  
Well Production ◽  
Monitoring Well ◽  
Oil And Gas Operations ◽  
Manual Interaction ◽  
Structured Literature Review

The business processes and decisions of oil and gas operations generate large amounts of data, which causes surveillance engineers to spend more time gathering, and analyzing them. To do this manually is inefficient. Hence, this study is proposed to leverage on data driven surveillance by adopting the principle of management by exception (MBE). The study aims to minimize the manual interaction between data and engineers; hence will focus on monitoring well production performance through pre-determined parameters with set of rules. The outcome of this study is a model that can identify any deviations from the pre-set rules and the model will alert user for deviations that occur. The model will also be able to predict on when the well be offline if the problem keeps on persisting without immediate action from user. The objective of this paper is to present a literature review on the prediction and management by exception for the above mentioned well management. The results presented in this paper will help in the development of the proposed prediction and management model. The literature review was conducted based on structured literature review methodology, and a comparative study among the collected works is analyzed and presented in this paper.  

scholarly journals Optimizing Production Schedule of Coalbed Methane Wells Using a Stochastic Evolution Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Qiujia Hu ◽  
Xianmin Zhang ◽  
Xiang Wang ◽  
Bin Fan ◽  
Huimin Jia
Keyword(s):  
Production Rate ◽  
Coalbed Methane ◽  
Optimization Problem ◽  
Production Optimization ◽  
Production Performance ◽  
Production Schedule ◽  
Schedule Optimization ◽  
Well Production ◽  
Control Frequency ◽  
Optimized Schedule

Production optimization of coalbed methane (CBM) is a complex constrained nonlinear programming problem. Finding an optimal decision is challenging since the coal seams are generally heterogeneous with widespread cleats, fractures, and matrix pores, and the stress sensitivities are extremely strong; the production of CBM wells needs to be adjusted dynamically within a reasonable range to fit the complex physical dynamics of CBM reservoirs to maximize profits on a long-term horizon. To address these challenges, this paper focuses on the step-down production strategy, which reduces the bottom hole pressure (BHP) step by step to expand the pressure drop radius, mitigate the formation damage, and improve CBM recovery. The mathematical model of CBM well production schedule optimization problem is formulated. The objective of the optimization model is to maximize the cumulative gas production and the variables are chosen as BHP declines of every step. BHP and its decline rate constraints are also considered in the model. Since the optimization problem is high dimensional, nonlinear with many local minima and maxima, covariance matrix adaptation evolution strategy (CMA-ES), a stochastic, derivative-free intelligent algorithm, is selected. By integrating a reservoir simulator with CMA-ES, the optimization problem can be solved successfully. Experiments including both normal wells and real featured wells are studied. Results show that CMA-ES can converge to the optimal solution efficiently. With the increase of the number of variables, the converge rate decreases rapidly. CMA-ES needs 3 or even more times number of function evaluations to converge to 100% of the optimum value comparing to 99%. The optimized schedule can better fit the heterogeneity and complex dynamic changes of CBM reservoir, resulting a higher production rate peak and a higher stable period production rate. The cumulative production under the optimized schedule can increase by 20% or even more. Moreover, the effect of the control frequency on the production schedule optimization problem is investigated. With the increases of control frequency, the converge rate decreases rapidly and the production performance increases slightly, and the optimization algorithm has a higher risk of falling into local optima. The findings of this study can help to better understanding the relationship between control strategy and CBM well production performance and provide an effective tool to determine the optimal production schedule for CBM wells.

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