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Asphaltene Precipitation is a major issue in both upstream and downstream sectors of the Petroleum Industry. This problem could occur at different locations of the hydrocarbon production system i.e., in the reservoir, wellbore, flowlines network, separation and refining facilities, and during transportation process. Asphaltene precipitation begins due to certain factors which include variation in crude oil composition, changes in pressure and temperature, and electrokinetic effects. Asphaltene deposition may offer severe technical and economic challenges to operating Exploration and Production companies with respect to losses in hydrocarbon production, facilities damages, and costly preventive and treatment solutions. Therefore, asphaltene stability monitoring in crude oils is necessary for the prevention of aggravation of problem related to the asphaltene deposition. This study will discuss the performance of eleven different stability parameters or models already developed by researchers for the monitoring of asphaltene stability in crude oils. These stability parameters include Colloidal Instability Index, Stability Index, Colloidal Stability Index, Chamkalani’s stability classifier, Jamaluddin’s method, Modified Jamaluddin’s method, Stankiewicz plot, QQA plots and SCP plots. The advantage of implementing these stability models is that they utilize less input data as compared to other conventional modeling techniques. Moreover, these stability parameters also provide quick crude oils stability outcomes than expensive experimental methods like Heithaus parameter, Toluene equivalence, spot test, and oil compatibility model. This research study will also evaluate the accuracies of stability parameters by their implementation on different stability known crude oil samples present in the published literature. The drawbacks and limitations associated with these applied stability parameters will also be presented and discussed in detail. This research found that CSI performed best as compared to other SARA based stability predicting models. However, considering the limitation of CSI and other predictors, a new predictor, namely ANJIS (Abdus, Nimra, Javed, Imran & Shaine) Asphaltene stability predicting model is proposed. ANJIS when used on oil sample of different conditions show reasonable accuracy. The study helps Petroleum companies, both upstream and downstream sector, to determine the best possible SARA based parameter and its associated risk used for the screening of asphaltene stability in crude oils.

Swelling of shale potentially occurs when it is exposed to water-based drilling fluid. The migration of hydrogen ions (H+) in the nano-interlayered platelets of the shale rock is utterly responsible for the swelling behavior in the shale. Conventionally, swelling behavior of any shale formation can be experimentally determined by linear dynamic swell meter. However, it is extremely important to validate these experimental results; hence, this research study aims in conducting a comparative performance analysis for different kinetic models, namely Peleg’s model, first-order exponential association equation and pseudo-second-order kinetic model, and a newly developed scaling swelling model in estimating the experimental results of three different shale samples, namely Talhar, Ranikot and Murree, obtained from different regions of Pakistan. It was found that the performance of the scaling swelling model was the most accurate in predicting the experimental swelling results with accuracy greater than 95% in all the three samples. Peleg’s model is found to be the most inaccurate with $$p \mathrm{values}< \alpha (0.05)$$ p values < α ( 0.05 ) in all the three formations. The equilibrium state in all the three samples was unable to attain by the use of this model. This clearly shows that the transient states continue throughout the course of experimentation, thus demonstrating a higher water activity in the shale samples. Moreover, when comparison was made between the two remaining kinetic adsorption models, it was perceived that pseudo-second-order kinetic was far superior to first-order exponential association equation with $${\mathrm{mean}}_{\mathrm{model}}\simeq {\mathrm{mean}}_{\mathrm{experiment}}$$ mean model ≃ mean experiment and less dispersion in the dataset. Nevertheless, the performance of this model also suffers with the increase in clay content. Furthermore, all these analyses were further validated by different statistical error analysis that includes MAE, APRE% and ANOVA.

Academic Laboratories plays a significant role during students and researchers academic life. In academic labs, students routinely performed different experiments and most often they are not aware of the experimental hazards and hence encounter accidents. Moreover, these research Labs possess complex nature because of their variety of operations, equipment and experiments, therefore, analyzing risks through conventional techniques become extremely difficult. In this research study, risks prioritization of an experiment related to asphaltene quantification in a crude oil has been carried out through four methods. Firstly, the conventional risk analysis technique Failure mode and effect analysis (FMEA) is applied. For the implementation of FMEA, 44 failure scenarios/modes that could occur and can impact health and safety of researcher or students are identified. Each failure mode is assessed in terms of severity, likelihood of occurrence and detection. FMEA, similar to other studies in literature, is not able to work effectively in prioritizing the risks. Therefore, in order to overcome this limitation of FMEA, FMEA integrated with Grey Relational Analysis and Fuzzy Analytic hierarchy process method is applied and improvement in risk ranking is observed. Finally for further analysis of risks, Fuzzy technique for order preference by similarity to ideal solution (TOPSIS) integrated with a Fuzzy AHP is implemented and considerable improvement in risk ranking is achieved. The proposed study will help researchers to take complete precautionary measures according to the risks involved in an experiment. Moreover, this proposed model can be used and applied for other experiments performed in Academic Labs.

This research study aims to conduct a comparative performance analysis of different scaling equations and non-scaling models used for modeling asphaltene precipitation. The experimental data used to carry out this study are taken from the published literature. Five scaling equations which include Rassamadana et al., Rassamdana and Sahimi, Hu and Gou, Ashoori et al., and log–log scaling equations were used and applied in two ways, i.e., on full dataset and partial datasets. Partial datasets are developed by splitting the full dataset in terms of Dilution ratio (R) between oil and precipitant. It was found that all scaling equations predict asphaltene weight percentage with reasonable accuracy (except Ashoori et al. scaling equation for full dataset) and their performance is further enhanced when applied on partial datasets. For the prediction of Critical dilution ratio (Rc) for different precipitants to detect asphaltene precipitation onset point, all scaling equations (except Ashoori et scaling equation when applied on partial datasets) are either unable to predict or produce results with significant error. Finally, results of scaling equations are compared with non-scaling model predictions which include PC-Saft, Flory–Huggins, and solid models. It was found that all scaling equations (except Ashoori et al. scaling equation for full dataset) either yield almost the same or improved results for asphaltene weight percentage when compared to best case (PC-Saft). However, for the prediction of Rc, Ashoori et al. scaling equation predicts more accurate results as compared to other non-scaling models.

One of the most significant problems in oil and gas sector is the swelling of shale when it comes in contact with water. The migration of hydrogen ions (H+) from the water-based drilling fluid into the platelets of shale formation causes it to swell, which eventually increases the size of the shale sample and makes it structure weak. This contact results in the wellbore instability problem that ultimately reduces the integrity of a wellbore. In this study, the swelling of a shale formation was modeled using the potential of first order kinetic equation. Later, to minimize its shortcoming, a new proposed model was formulated. The new model is based on developing a third degree polynomial equation that is used to model the swelling percentages obtained through linear dynamic swell meter experiment performed on a shale formation when it comes in contact with a drilling fluid. These percentages indicate the hourly change in sample size during the contact. The variables of polynomial equation are dependent on the time of contact between the mud and the shale sample, temperature of the environment, clay content in shale and experimental swelling percentages. Furthermore, the equation also comprises of adjustable parameters that are fine-tuned in such a way that the polynomial function is best fitted to the experimental datasets. The MAE (mean absolute error) of the present model, namely Scaling swelling equation was found to be 2.75%, and the results indicate that the Scaling Swelling equation has the better performance than the first order kinetics in terms of swelling predication. Moreover, the proposed model equation is also helpful in predicting the swelling onset time when the mud and shale comes in direct contact with each other. In both the cases, the percentage deviation in predicting the swelling initiation time is close to 10%. This information will be extremely helpful in forecasting the swelling tendency of shale sample in a particular mud. Also, it helps in validating the experimental results obtained from linear dynamic swell meter.

Over a past few decades, the major contemplation in the petroleum industry has been to develop a drilling fluid that minimizes the wellbore instability problems and shows high performance in drilling shale formations. This paper focuses on the formulation of a novel water-based drilling fluid using nano-particle graphene oxide (GO) and micron particle Pure-bore in varying concentration ranging from 0.1 to 0.5 wt%. To prove their efficacy, mud containing these particles was used to mitigate the swelling characteristics of the Murree shale formation obtained from the Northern region of Pakistan. Results reveal that the addition of GO and Pure-bore in different concentrations in water-based drilling fluid, significantly improves the rheological properties, filtration characteristics and swelling inhibition of the base mud. Almost 50% swelling of the Murree formation was reduced while using these two particles. Additionally, when the comparison was done between the two particles, it was concluded that GO mud provides a better filtrate control mud and swelling inhibition in the shale formation than Pure-bore. The reason of its better performance is because it develops a better membrane like mud cake having a better surface quality, toughness and stiffness. Moreover, the active functional group especially hydroxyl and carboxyl present in GO interacts actively with Smectite clay mineral with the help of hydrogen and chemical bonds resulting in a better filtration control. These properties of GO ensure that the hydrogen ions do not penetrate into the nano-spacing of the formation thus providing a better wellbore stability in Murree shale formation.