Skip to main content

Research Repository

Advanced Search

Genetic programming application in predicting fluid loss severity.

Amish, Mohamed; Etta-Agbor, Eta


Eta Etta-Agbor


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 utilised 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 utilised. 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.


AMISH, M. and ETTA-AGBOR, E. 2023. Genetic programming application in predicting fluid loss severity. Results in engineering [online], 20, article number 101464. Available from:

Journal Article Type Article
Acceptance Date Sep 26, 2023
Online Publication Date Oct 2, 2023
Publication Date Dec 31, 2023
Deposit Date Oct 5, 2023
Publicly Available Date Oct 5, 2023
Journal Results in engineering
Electronic ISSN 2590-1230
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 20
Article Number 101464
Keywords Lost circulation; Machine learning; Multigene genetic algorithms; Drilling; Non-productive time
Public URL


You might also like

Downloadable Citations