Iranian Journal of Oil and Gas Science and Technology

Abstract Lost circulation of drilling mud is one of the most significant challenges during drilling operations, leading to time loss and increased costs. It is influenced by factors such as the lithology and composition of the formation, the pressure difference between the drilling mud hydrostatics and the formation, the type of drilling mud, and hydraulic conditions. In the Asmari Formation of the Maroun Oil Field in Iran, lost circulation commonly occurs due to natural fractures, cavernous structures, and drilling-related factors. Several methods exist to mitigate this problem, including the use of appropriate loss circulation materials (LCMs) and reducing the drilling mud density. In this study, oyster shell and nanographene are proposed as LCM additives for optimized oil-based drilling mud in the Maroun Oil Field, and the necessary experiments were conducted. This paper investigates the simultaneous use of oyster shell as a normal particle-sized material and nanographene as a nanoparticle-sized material to control mud loss. The results indicate that nanographene is unable to seal fractures larger than 0.04 inches, making its use for wider fractures uneconomical. Oyster shell is ineffective as an LCM for fractures wider than 0.08 inches but performs well in sealing fractures smaller than 0.08 inches.

Abstract Stimulation of hydrocarbon wells with matrix acidizing operation is among the most common operations to stimulate the formation, remove the skin, and improve the productivity index. However, equipment corrosion, including casings, is one of the most critical concerns. In the present paper, the influence of the magnetic field on the corrosion behavior of drilling casing in 1.5 M (5 wt %) HCl was investigated in various conditions using potentiodynamic polarization (PDP) and weight loss (WL) measurements. The Taguchi experimental design (L-18 array) was utilized to model the impacts of magnetic field intensity, elapsed time, magnetization time, and temperature on the corrosion rate. The experimental results showed that the passing of acid through a magnetic field reduced the corrosion rate of N-80 carbon steel in HCl by up to 96%. Consequently, magnetized acid could reduce the effects of corrosion on matrix acidizing operations as a green corrosion inhibitor.

Abstract Fishing operations are one of the most essential parts of drilling operations. If the fishing operation fails, the other direction should be considered to continue drilling and reach the desired depth, which can be achieved using sidetracking operations. Long-term fishing operations increase the cost and time of the drilling operation; therefore, we should try to have a successful fishing operation in the shortest possible time. It can be stated that the execution of the fishing operation is economical as long as the costs are less or at least equal to the cost of the sidetracking operation. Therefore, the optimal fishing time must be determined to make the drilling operation economical. Many statistical analysis methods have been used to determine the optimal time, but they are not popular due to insufficient accuracy and time-consuming calculations. This study used a machine-learning (ML) model with a regression algorithm to estimate the optimal time for fishing operations in the Gachsaran oil field. The fishing cost rate and depth as input data were first collected and categorized based on different sections of the Gachsaran oil field to calculate the optimal fishing time. Then, the sidetracking cost was predicted by the machine learning model, and this cost was equated to the fishing cost in the worst conditions. As a result, the optimal fishing time was calculated for each section. The result showed that the model could estimate the cost of sidetracking with an error of less than 2%. Using the designed model and the input data of the Gachsaran oil field, considering the optimal fishing time, it was possible to save $1 million and 16 h in drilling a well.

Abstract A successful drilling operation requires an effective drilling fluid system. The aim of this work is to provide an effective solution for improving the rheological and filtration properties of water-based drilling fluid by using CuO nanofluid additive. CuO nanoparticles were synthesized by hydrothermal method using autoclave, which can control the temperature as well as pressure. Then CuO nanofluid (eco-friendly ethylene glycol based) were produced to use as a drilling fluid additive. X-ray diffraction, Fourier-transformed infrared, scanning electron microscope were used to characterize nanoparticles. The results confirmed clearly the formation of high purity CuO nanoparticles forming a wire shape structure. The operating parameters were optimized by experimental design method and based on the optimal results, two long time stabilized nanofluids were prepared to improve the rheological properties and the fluid loss of a polymeric water-based drilling fluid. Xanthan, polyanionic cellulose and starch are commonly used in drilling fluids to improve rheological and fluid loss properties. Also, the effect of pH level of nanofluids on the improvement of water-based drilling fluid properties was investigated. The results showed that the nanofluid with pH=8 can be used as the best additive to improve the drilling fluid properties. The improvement of the yield point, apparent viscosity, 10-second and 10-minute gel strengths of the drilling fluid as well as the fluid loss were 45, 33, 200, 100 and 44 %, respectively.

Abstract Among the different operating parameters that must be carefully controlled during the drilling operation, penetration of drilling mud into the permeable zone of formations is one of the essential ones that can have a destructive effect on the productive zone. Thus, 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 the properties of the drilling fluid, including rheology and high- and low-temperature filtration. NPs 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 NPs with a concentration of 2 wt % are added to the drilling fluid. Extreme 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 reducing the gelation properties of the drilling mud is possible using low concentrations of NPs. Moreover, the results reveal that SiO₂ and ZnO exhibit a lower filtration rate than CuO. Finally, the effects of temperature and pressure were investigated, which revealed that regardless of the reductive effect of NPs (reducing the filtration rate from 17.7 to about 10 cm³), increasing the pressure and temperature lead to an increase in the filtration rate (reducing the filtration rate from 67 to 35 cm³). Further, the rheological properties of the mud remain relatively constant.

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 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.

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. 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.

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 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.