Horizontal Well Production Optimization Using Production Logs Run on Coiled Tubing in the 26R Sand Reservoir, Stevens Zone, Elk Hills Field, California

1996 ◽  
Author(s):  
Charles W. Walker ◽  
Steven A. Garcia ◽  
E. Mark Querin ◽  
David M. Moore
Keyword(s):  
Horizontal Well ◽  
Production Optimization ◽  
Well Production ◽  
Coiled Tubing

Novel Coiled Tubing Perforation Approach Enables Efficient Multistage Hydraulic Fracturing Operations in a Horizontal Well in Oman

2016 ◽  
Author(s):  
Ali Al-Ghaithi ◽  
Fahad Alawi ◽  
Ernest Sayapov ◽  
Ehab Ibrahim ◽  
Najet Aouchar ◽  
...  
Keyword(s):  
Hydraulic Fracturing ◽  
Horizontal Well ◽  
Multistage Hydraulic Fracturing ◽  
Coiled Tubing

Challenges for Coiled Tubing in Slim Hole Horizontal Well Intervention

2009 ◽  
Author(s):  
Hamed Hamoud Al-Sharji ◽  
Erik Ferdiansyah ◽  
Santhana Kumar ◽  
Fardin Ali Neyaei
Keyword(s):  
Horizontal Well ◽  
Coiled Tubing

Multidisciplinary Approach in The Permian Basin - A Geological, Statistical and Engineering Case Study to Production Results on the Wichita-Albany Formation

2015 ◽  
Author(s):  
Fabián Vera ◽  
Casee Lemons ◽  
Ming Zhong ◽  
William D. Holcomb ◽  
Randy F. LaFollette
Keyword(s):  
Design Patterns ◽  
Point Of View ◽  
Production Optimization ◽  
Multivariate Statistical ◽  
Data Set ◽  
Modern Approach ◽  
Well Production ◽  
Fracture Fluid ◽  
Major Factors ◽  
The Impact

Abstract This study compares reservoir characteristics, completion methods and production for 431 wells in 6 counties producing from the Wichita-Albany reservoir to assess major factors in production optimization and derive ultimate recovery estimates. The purpose of the study is to analyze completion design patterns across the study area by combining public and proprietary data for mining. Integrating several analyses of different nature and their respective methods like statistics, geology and engineering create a modern approach as well as a more holistic point of view when certain measurements are missing from the data set. Furthermore, multivariate statistical analysis allows modeling the impact of particular completion and stimulation parameters on the production outcome by averaging out the impact of all other variables in the system. In addition to completion type, more than 18 predictor variables were examined, including treatment parameters such as fracture fluid volume, year of completion, cumulative perforated length, proppant type, proppant amount, and county location, among others. In this sense, this contribution seems unique in unifying statistical, engineering, and geological perspectives into a singular point of view. This work also provides complementary views for well production consideration.

scholarly journals Production Optimization for Natural Flow and ESP Well A Case Study on Well NS-5 Mishrif Formation-Nasriya Oil Field

2020 ◽  
Vol 10 (2) ◽  
pp. 17-35
Author(s):  
Hamzah Amer Abdulameer ◽  
Dr. Sameera Hamd-Allah
Keyword(s):  
Production Rate ◽  
Production Optimization ◽  
Oil Field ◽  
Reservoir Pressure ◽  
Pump Frequency ◽  
Individual Parameter ◽  
Well Production ◽  
Well Design ◽  
Natural Flow ◽  
Reservoir Conditions

As the reservoir conditions are in continuous changing during its life, well production rateand its performance will change and it needs to re-model according to the current situationsand to keep the production rate as high as possible.Well productivity is affected by changing in reservoir pressure, water cut, tubing size andwellhead pressure. For electrical submersible pump (ESP), it will also affected by numberof stages and operating frequency.In general, the production rate increases when reservoir pressure increases and/or water cutdecreases. Also the flow rate increase when tubing size increases and/or wellhead pressuredecreases. For ESP well, production rate increases when number of stages is increasedand/or pump frequency is increased.In this study, a nodal analysis software was used to design one well with natural flow andother with ESP. Reservoir, fluid and well information are taken from actual data of Mishrifformation-Nasriya oil field/ NS-5 well. Well design steps and data required in the modelwill be displayed and the optimization sensitivity keys will be applied on the model todetermine the effect of each individual parameter or when it combined with another one.

Underbalanced Coiled Tubing Drilled Horizontal Well in the North Sea

1996 ◽  
Vol 48 (5) ◽  
Author(s):  
Philip Wodka ◽  
Henrik Tirsgaard ◽  
C.J. Adamsen ◽  
A.P. Damgaard
Keyword(s):  
North Sea ◽  
Horizontal Well ◽  
Coiled Tubing ◽  
The North ◽  
The North Sea

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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