HYDRAULIC FRACTURING CANDIDATE-WELL SELECTION USING ARTIFICIAL INTELLIGENCE APPROACH

Agus Aryanto, Sugiatmo Kasmungin, Fathaddin M. T.

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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DOI: http://dx.doi.org/10.25105/semnas.v0i0.2487

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