Petroleum Engineering – Drilling
Afshar Alihosseini; Ali Hassan Zadeh; Majid Monajjemi; Mahdi Nazary Sarem
Abstract
Wellbore stability is of one of challenges in drilling industry. Shale formation is of one of the most problematic rocks during drilling because the rock has very low permeability and very small pores (nanometers). In this study, the viability of the Alumina nanoparticles in Water-Based Mud (WBM) is ...
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Wellbore stability is of one of challenges in drilling industry. Shale formation is of one of the most problematic rocks during drilling because the rock has very low permeability and very small pores (nanometers). In this study, the viability of the Alumina nanoparticles in Water-Based Mud (WBM) is assessed. The effect of Alumina nanoparticles (alpha and gamma) as mud additives to improve the rheological properties in water- base drilling fluids are experimentally investigated. The Alumina nanoparticles has specific chemical and physical properties such as: high compressive strength, high hardness, high thermal conductivity, these properties improve the rheological properties of water base drilling fluid, reduce filtration loss and meet environmental regulations. The results of experiments indicated that Alumina nanoparticle are improving rheological properties such as: yield point (YP), gel strength (GEL 10s, Gel 10min) of water- base drilling, that can be used to improve the rheological and filtration properties of drilling fluids. The experimental data showed that, Alumina nanoparticle as a Nano additive, possessed excellent properties such as thermal stability, rheology enhancing, fluid loss control and lubrication. Also, it could plug the shale formation effectively and improve the pressure bearing capability of formation significantly. In addition, Alumina nanoparticles reduced 60% API/HPHT fluid loss by 60% in comparison with blank sample. The most striking feature is that shale integrity improved by Nano fluid between 60 and 70 percent in comparison with blank sample. Also, data experimental of CT Scan are shown that the filter cakes formed by each of the Nano-drilling fluid samples with alpha alumina and gamma alumina base are more cohesive and cause an integrated filter cake on the well.
Petroleum Engineering – Drilling
Hossein Yavari; Mohammad Sabah; Rassoul Khosravanian; David. A Wood
Abstract
The rate of penetration (ROP) is one of the vital parameters which directly affects the drilling time and costs. There are various parameters that influence the drilling rate; they include weight on bit, rotational speed, mud weight, bit type, formation type, and bit hydraulic. Several approaches, including ...
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The rate of penetration (ROP) is one of the vital parameters which directly affects the drilling time and costs. There are various parameters that influence the drilling rate; they include weight on bit, rotational speed, mud weight, bit type, formation type, and bit hydraulic. Several approaches, including mathematical models and artificial intelligence have been proposed to predict the rate of penetration. Previous research has showed that artificial intelligence such as neural network and adaptive neuro-fuzzy inference system are superior to conventional methods in the prediction of drilling rate. On the other hand, many complicated analytical ROP models have also been developed during recent years that are able to predict drilling rate with a high degree of accuracy. Therefore, comparing different approaches to find the most accurate model and assess the conditions in which each model works well can be highly effective in reducing drilling time as well as drilling cost. In this study, Hareland-Rampersad (HR) model, Bourgoyne and Young (BY) model, and an adaptive-neuro-fuzzy inference system (ANFIS) are employed to predict the drilling rate in the South Pars gas field (SP) offshore of Iran, and their results are compared to find the best ROP-prediction model for each formation. A database covering the drilling parameters, sonic log data, and modular dynamic test data collected from several drilling sites in SP are used to construct the mentioned models for each formation. The results show that when a large amount of data is available, the ANFIS is more accurate than the other approaches in predicting drilling rate. In the case of ROP models, BY model works considerably better than HR model for the majority of the formations. However, in formations where some drilling parameters are constant, but formation strength is variable, HR model shows better prediction performance than BY model.
