Lost circulation or losses in drilling fluid is one of the most important problems in the oil and gas industry, and it appeared at the beginning of this industry, which caused many problems during the drilling process, which may lead to closing the well and stopping the drilling process. The drilling muds are relatively expensive, especially the muds that contain oil-based mud or that contain special additives, so it is not economically beneficial to waste and lose these muds. The treatment of drilling fluid losses is also somewhat expensive as a result of the wasted time that it caused, as well as the high cost of materials used in the treatment such as heavy materials, cement, and others. The best way to deal with drilling fluid losses is to prevent them. Drilling fluid loss is a complex problem that is difficult to predict using simple and traditional methods. Artificial intelligence represents a modern and accurate technology for solving complex problems such as drilling fluid loss. Artificial intelligence through supervised machine learning provides the possibility of predicting these losses before they occur based on field data such as drilling fluid properties, drilling parameters, rock properties, and geomechanical parameters that are related to the loss of circulation of the wells suffered from losses problem located in the same area. In this paper, several supervised machine learning models have been reviewed that were used for detecting and predicting of loss of drilling fluids during the drilling process. The paper provides an inclusive review of drilling fluid prediction and detection from simplest to more complected intelligent models.
This study was completed to investigate the breed factor that influences milk production in different goat breeds in the tropical environmental area. A total of 28 goats were utilized in this work. These goats consisted of two different breeds, which were Anglo-Nubian and Saanen. Blood samples were withdrawn through jugular veinsof goats into plastic tubes without EDTA for gene polymorphism. A t-test was used to analyse if there are any significant differences in molecular weight and frequency values of DNA between breeds. Investigation of DNA polymorphism in alleles A, B and C of α-casein were identified in all breeds, while allele F were studied in Saanen goats only. As a conclusion; Saanen goats have more varieties α-casein allele
Drilling soft and fragile areas such as high permeable, cavernous, fractured, and sandy formations are often accompanied by many problems. One of the most important of these problems is the loss of drilling fluid into these formations in whole or in part. The loss of drilling fluid can lead to bigger and more complex problems, including pipe stucking or kicking and finally closing the well. The drilling muds are relatively expensive, especially the muds that contain oil-based mud or that contain special additives, so it is not economically beneficial to waste and lose these muds. The treatment of drilling fluid losses is also somewhat expensive as a result of the wasted time that it caused, as well as the high cost of materials used in the treatment such as heavy materials, cement, and others. The best way to deal with drilling fluid losses is to prevent them. Artificial intelligence through supervised machine learning provides the possibility of predicting these losses before they occur based on equivalent circulating density, yield point, plastic viscosity, rate of penetration, flow rate, and losses rate of wells suffered from losses problem located in the same area. In this paper, three supervised machine learning models, namely, decision tree, random forest, and extra trees, were built to predict drilling fluid losses in the Rumaila oil field in southern Iraq. The results show that the extra trees model with an R2 of 0.9681was able to predict the new lost circulation events with high accuracy.
The Zubair oil field is predominantly plagued by geomechanical issues, which can result in significant non-productive time. The aim of this study is to present a reservoir model and a geomechanical model utilizing the finite element method. The intriguing data consisting of logs, calibration data, drilling reports, and mud reports were utilized to construct one-dimensional models (1D) for each well using Techlog 2015 software. Furthermore, the 3D geomechanical model was built utilizing Petrel 2017 software, while the finite element technique was implemented using the CMG 2018 program to predict the total stress states during production or injection operations in the field over a span of 10 years.The analysis results of all mechanical rock properties in the 3D geomechanical model revealed that Shuaiba and Al-Hammar domes were insufficient for maintaining stable wells, particularly in the Tanuma formation. However, the Mishrif formation displayed higher stability despite production. Furthermore, the 3D finite element model exhibited that the total horizontal stress decreased during production and increased in injection wells. This variation would result in an increase in the effective horizontal stress during production and a decrease in injection wells. Moreover, the effective vertical stress increased during production and decreased during injection wells. Based on these outcomes, it can be concluded that production could trigger an increase in the differential stress leading to rock shear failure, whereas in injection cases, pore pressure increased, and this caused tensile failure.
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