Petroleum Engineering – Drilling
Abdeslem Leksir
Abstract
Column final test facing new challenge, in addition to casing burst/collapse limitations; buckling occurrence creates serious problems. Practically, in case of slight gap between mud and slurry densities, buckling initiation is inevitable. Casing elongation, bending and buckling detailed, to define column ...
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Column final test facing new challenge, in addition to casing burst/collapse limitations; buckling occurrence creates serious problems. Practically, in case of slight gap between mud and slurry densities, buckling initiation is inevitable. Casing elongation, bending and buckling detailed, to define column behavior while testing. Buckling influences on slurry pointed out too, and compared to column without test. New cement quality indicator is proposed, tested and validated via logging of wells drilled in different regions. Results generalized to cover other situations rather than heavy sections. Gas migration region, depleted reservoir, weak zones are all examined. Registrations confirm the appearance of buckling either while pumping slurry or pressure testing. New modified casing selection method conjointly with updated numerical technique proposed to prevent buckling. Experimental and simulation findings confirm the reliability of the proposed technique.
Petroleum Engineering – Drilling
Borzu Asgari pirbalouti
Abstract
Among the different operating parameters that must be carefully controlled during the drilling operation, the penetration of drilling mud into a permeable zone of formations is an essential parameter that can introduce a destructive effect on the productive zone. Concerning this, the current investigation ...
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Among the different operating parameters that must be carefully controlled during the drilling operation, the penetration of drilling mud into a permeable zone of formations is an essential parameter that can introduce a destructive effect on the productive zone. Concerning this, the current investigation concentrates on investigating the effects of different nanoparticles (NPs), namely SiO2, CuO, and ZnO considering their size, type, and concentration (0.2 to 2 wt % for each nanoparticle) on drilling fluid properties, including rheology and high- and low-temperature filtration. Nanoparticles can improve the rheological properties of the mud by changing the friction coefficient favorably. Moreover, the effects of temperature and pressure as two critical thermodynamic parameters are examined. The results show that it is possible to enhance the rheological properties (viscosity) of the drilling mud to a maximum value of about 20% if nanoparticles with a concentration of 2 wt % are added to the drilling fluid. Excessive gel strength will lead to high pump initiation pressure to break circulation after the mud is in a static condition for some time. The results reveal that it is possible to reduce the gelation properties of the drilling mud using low concentrations of nanoparticles. Moreover, the obtained results demonstrate that among the examined nanoparticles, SiO2 and ZnO exhibit a lower filtration rate than CuO. Finally, the effects of temperature and pressure show that regardless of the reductive effect of nanoparticles (reducing the filtration rate from 17.7 to about 10 cm3), increasing the pressure and temperature leads to an increase in the filtration rate (reducing the filtration rate from 67 to 35 cm3). Further, mud rheological properties remain relatively constant.
Petroleum Engineering – Drilling
Aref Khazaei; Reza Radfar; Abbas Toloie Eshlaghy
Abstract
Iran is one of the largest oil and gas producers in the world. Intelligent manufacturing approaches can lead to better performance and lower costs of the well drilling process. One of the most critical issues during the drilling operation is the wellbore stability. Instability of wellbore can occur at ...
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Iran is one of the largest oil and gas producers in the world. Intelligent manufacturing approaches can lead to better performance and lower costs of the well drilling process. One of the most critical issues during the drilling operation is the wellbore stability. Instability of wellbore can occur at different stages of a well life and inflict heavy financial and time damage on companies. A controllable factor can prevent these damages by selecting a proper drilling mud weight. This research presents a drilling mud weight estimator for Iranian wells using deep-learning techniques. Our Iranian data set only contains 900 samples, but efficient deep-learning models usually need large amounts of data to obtain acceptable performance. Therefore, the samples of two data sets related to the United Kingdom and Norway fields are also used to extend our data set. Our final data set has contained more than half-million samples that have been compiled from 132 wells of three fields. Our presented mud weight estimator is an artificial neural network with 5 hidden layers and 256 nodes in each layer that can estimate the mud weight for new wells and depths with the mean absolute error (MAE) of smaller than ±0.039 pound per gallon (ppg). In this research, the presented model is challenged in real-world conditions, and the results show that our model can be reliable and efficient in the real world.
Petroleum Engineering – Drilling
Afshar Alihosseini; Ali Hassan Zadeh; Majid Monajjemi; Mahdi Nazary Sarem
Abstract
Wellbore stability is one of the challenges in the drilling industry. Shale formation is one of the most problematic rocks during drilling because the rock has very low permeability and tiny pores (nanometers). This study assesses the viability of the alumina nanoparticles (Al2O3) in water-based mud. ...
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Wellbore stability is one of the challenges in the drilling industry. Shale formation is one of the most problematic rocks during drilling because the rock has very low permeability and tiny pores (nanometers). This study assesses the viability of the alumina nanoparticles (Al2O3) in water-based mud. The effectiveness of alumina nanoparticles as a mud additive in improving the rheological properties in water-based drilling mud is investigated. The alumina nanoparticles have specific chemical and physical properties, such as high compressive strength, high hardness, and high thermal conductivity. These properties improve the properties of water-based drilling mud, reduce filtration loss, and meet environmental regulations. The results of experimental data show that alumina nanoparticle improves rheological properties such as yield point gel strength (GEL 10 s, Gel 10 min) of water-based drilling that can be utilized to enhance the significant feature of drilling mud, particularly in rheology and filtration. Preliminary data demonstrated that alumina nanoparticles, a nano additive, possess proper properties like thermal stability, rheology enhancement, fluid loss control, and lubrication. It is likely to encounter shale formation plug and significant improvement formation pressure. In addition, alumina nanoparticles reduced 60% API/HPHT fluid loss by 60% compared to the blank sample. The most striking feature is that nanofluid improved shale integrity between 60% and 70% compared to the blank sample. Further, the experimental data of the CT scan show that the mud cakes formed by each of fluid samples, including nanoparticles containing alpha- 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.
