Abstract
One of the most troublesome issues in the drilling industry is stuck drill pipes. Drilling activities will be costly and time-consuming due to stuck pipe issues. As a result, predicting a stuck pipe can be more useful. This study aims to use an artificial intelligence technology called hybrid particle swarm optimization neural network (PSO-based ANN) to predict the probability of a stuck pipe in a Middle East oil field. In this field, a total of 85 wells were investigated. Therefore, to predict this problem, we must examine and determine the role of drilling parameters by creating an appropriate model. In this case, an artificial neural network is used to solve and model the problem. In this way, by processing the parameters of wells with and without being stuck in this field, the stuck or non-stuck of drilling pipes in future wells is predicted. To create a PSO-based ANN model database, mud characteristics, geometry, hydraulic, and drilling parameters were gathered from well daily drilling reports. In addition, two databases for directional and vertical wells were established. There are two types of datasets used for each database: stuck and non-stuck. It was discovered that the PSO-based ANN model could predict the incidence of a stuck pipe with an accuracy of over 80% for both directional and vertical wells. This study divided data from several cases into four sections: 17 ½″, 12 ¼″, 8 ½″, and 6 1/8″. The key reasons for sticking and the mechanics have been thoroughly investigated for each section. The methodology presented in this paper enables the Middle East drilling industry to estimate the risk of stuck pipe occurrence during the well planning procedure.
Highlights
The most costly unplanned drilling occurrence for an operator is sticking a Drilling BHA, which results in the loss of equipment, hole footage, and maybe endangers well objectives
Stuck pipe events have the highest number of nonproductive times (NPT) in the drilling industry, ahead of well control incidents, waiting on the weather (WOW), lost circulation, equipment failures, and rig issues (Dushaishi et al 2020; Amadi 2015)
The research found that these networks are validated after 3401 epochs for the multilayer perceptron (MLP) network and 4264 epochs for the radial basis functions (RBF) network, and they can estimate the error of around 1%
Summary
The most costly unplanned drilling occurrence for an operator is sticking a Drilling BHA, which results in the loss of equipment, hole footage, and maybe endangers well objectives. In 2007, Miri et al conducted two models to predict differential sticking They built the database model by creating 109 datasets representing 61 differentially stuck pipe incidents and 48 non-stuck pipe events. Shadizadeh et al used an artificial neural network to predict the probability of a stuck pipe in 2010 They tested their model on databases with a total of 275 cases in them. Most of the drilled wells in this field, according to DDRs, have had at least one stuck difficulty during their drilling operation This issue has shown itself in every aspect of the well profile. According to DDRs in the desired oil field, the processes used to liberate pipes in some cases are exceedingly time-consuming and costly In this field, optimizing drilling parameters, mud characteristics, and geometry factors to reduce the danger of sticking can save time and money. The wellbore is divided into four portions: hole Sections. 17 1⁄2′′, 12 1⁄4′′, 8 1⁄2′′, and 6 1/8′′
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