HYDRAULIC FRACTURING CANDIDATE-WELL SELECTION USING ARTIFICIAL INTELLIGENCE APPROACH

Agus Aryanto, Sugiatmo Kasmungin, Fathaddin Fathaddin

Abstract


. Hydraulic fracturing is one of the stimulation method that aimed to increase productivity of well by creating a high conductive conduit in reservoir connecting it to the wellbore. This high conductivity zone is created by injecting fluid into matrix formation with enough rate and pressure. After crack initiate and propagate, the process continue with pumping slurry consist of fracturing fluid and sand. This slurry continues to extend the fracture and concurrently carries sand deeply into formation. After the materials pumped, carrier fluid will leak off to the formation and leave the sand holds the fracture created. TLS Formation in X and Y Field is widely known as a formation that have low productivity since it has low permeability around 5 md and low resistivity 3 Ohm-m. Oil from TLS formation could not be produced without fracturing. This formation also have high clay content, 20 – 40 % clay. Mineralogy analysis also shown that this formation contains water sensitive clay such as smectite and kaolinite. Hydraulic fracturing has been done in this field since 2002 on around 130 wells. At the beginning of hydraulic fracturing campaign, the success parameter is only to make the wells produce hydrocarbon in economical rate. As the fractured wells become larger in number, several optimization is also been done to increase oil gain. Later on, the needs of conclusive analysis to evaluate well performance after hydraulic fracturing rise up due to sharp decrement of crude oil price. Accurate analysis and recommendation need to be conducted to assess the best candidate for hydraulic fracturing to maximize success ratio. Even though a common practice, candidate-well selection is not a straightforward process and up to now, there has not been a well-defined approach to address this process. Conventional methods are not easy to use for nonlinear process, such as candidate-well selection that goes through a group of parameters having different attributes and features such as geological aspect, reservoir and fluid characteristics, production details, etc. and that’s because it is difficult to describe properly all their nonlinearities. In that matter, Artificial Intelligence approach is expected to be an alternative solution for this condition.


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References


Behzad Mehrgini, Hossein Memarian, Akbar Fotouhi, Mozhgan Moghnainan, “Recognising the Effective Parameters and their Influence on Candidate-Well Selection for Hydraulic Fracturing Treatment by Decision Making Method”, International Petroleum Technology Conference, Kuala Lumpur, Malaysia, December 2014

Economides, M.J. and Martin, T., Modern Fracturing, Enhancing Natural Gas Production, ET Publishing, 2007

Mansoor Zoveidavianpoor, Ariffin Samsuri, Seyed Reza Shadizadeh, “Development of a Fuzzy System Model for Candidate-well Selection for Hydraulic Fracturing in a Carbonate Reservior”, SPE Oil and Gas India Conference and Exhibition, Mumbai, India, March 2012

Mansoor Zoveidavianpoor, Ariffin Samsuri, Seyed Reza Shadizadeh, “A Review on Conventional Candidate-well Selection for Hydraulic Fracturing in Oil and Gas Wells”, International Journal of Engineering and Technology Volume 2 No. 1, January, 2012

Mansoor Zoveidavianpoor, Ariffin Samsuri, “Hydraulic Fracturing Candidate-Well Selection by Interval Type-2 Fuzzy Set and System”, International Petroleum Technology Conference, Beijing, China, March 2013

Martin, A.N and Economides, M.J. “Best Practices for Candidate Selection, Design and Evaluation of Hydraulic Fracturing Treatments”, SPE 135669

Nicolas P. Roussel, Mukul M. Sharma, “Selecting Candidate Wells for Refracturing Using Production Data”. SPE Annual Technical Conference and Exhibition, Denver, Colorado, USA, 30 October – 2 November 2011.


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