Genetic programming application in predicting fluid loss severity
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- @Article{AMISH:2023:rineng,
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author = "Mohamed Amish and Eta Etta-Agbor",
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title = "Genetic programming application in predicting fluid
loss severity",
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journal = "Results in Engineering",
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volume = "20",
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pages = "101464",
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year = "2023",
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ISSN = "2590-1230",
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DOI = "doi:10.1016/j.rineng.2023.101464",
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URL = "https://www.sciencedirect.com/science/article/pii/S2590123023005911",
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keywords = "genetic algorithms, genetic programming, Lost
circulation, Machine learning, Multigene genetic
algorithms, Drilling. non-productive time",
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abstract = "Numerous wells worldwide encounter significant,
costly, and time-consuming lost circulation issues
during drilling or while deploying tubulars across
naturally fractured or induced fractured formations.
This can potentially lead to formation damage, wellbore
instability, and even blowouts. Effectively addressing
this problem and restoring fluid circulation becomes
crucial to curbing non-productive time and overall
operational expenses. Although numerous methods have
been introduced, a universally accepted industry
solution for predicting lost circulation remains absent
due to the complex interplay of various factors
influencing its severity. Anticipating the onset of
circulation loss is imperative to mitigate its impacts,
minimise costs, and reduce risks to personnel and the
environment. In this study, an innovative machine
learning approach employing multigene genetic
algorithms is used to analyse a dataset of 16,970
drilling datasets from 61 wells within the Marun oil
field, located in Iran, where severe loss of
circulation occurred. Geological characteristics,
operational drilling parameters, and the properties of
the drilling fluid were all considered. The dataset
encompasses 19 parameters, of which seven are chosen as
inputs for predicting lost circulation incidents. These
inputs are then employed to construct a predictive
model, employing an 85:15 training-to-test data ratio.
To assess the model's performance, unseen datasets are
used. The novelty of this study lies in the proposed
model's consideration of a concise set of relevant
input parameters, particularly real-time surface
drilling parameters that are easily accessible for
every well. The model attains a remarkable level of
prediction accuracy for fluid loss, as indicated by
various performance indices. The results indicate a
mean absolute error of 1.33, a root mean square error
of 2.58, and a coefficient of determination of 0.968.
The suggested prediction model is optimised not only
for data reduction but also for universal prediction
and compatibility with other existing platforms.
Moreover, it aids drilling engineers in implementing
suitable mitigation strategies and designing optimal
values for key operational surface parameters, both
prior to and during drilling operations",
- }
Genetic Programming entries for
Mohamed Amish
Eta Etta-Agbor
Citations