This paper proposed a model that explains the separation mechanism of oil-in-water emulsion taking into account both creaming and coalescence processes. Oil-in-water emulsion separation experiments were performed in a batch separator using kerosene and distilled water. The predictions of the proposed model agreed well with the experimental results as well as previously published experimental data. The comparison between the proposed model and the previously published model showed that the proposed model has higher accuracy in predicting the separation profile of oil-in-water emulsion, with an accuracy of within 10%. Thus the proposed model gives better representation of the oil-in-water emulsion separation process.

Accurate estimation of formation conditions plays a pivotal role in effectively managing various processes related to hydrates, including flow assurance, deep-water drilling, and hydrate-based technology development. The formation temperature of methane hydrates in the presence of brine greatly affects the efficacy and accuracy of these processes. This work presents a comprehensive and novel comparative analysis of nine distinct machine learning models for accurate prediction of formation temperatures of methane hydrate. This study investigated the application of major machine learning (ML) algorithms including multiple linear regression (MLR), long short-term memory (LSTM), radial basis function (RBF), support vector machine (SVM), artificial neural network (ANN), gradient boosting regression (GBR), gradient process regression (GPR), random forest (RF), and K-nearest neighbor (KNN). The model accuracy was validated against a large dataset comprising of over 1000 data points with diverse range of salt concentrations. In this regard, model accuracies were compared using several metrics including R², ARD, and AARD. The experimental results exhibited KNN algorithm to be fast-converging, accurate, and consistent over the entire range of data points with an R² score of 0.975 and AARD of 0.385%. The results enable efficient and accurate temperature estimation with ML algorithms for multiple hydrate-related processes.

Alkalis, surfactants and/or polymers are usually injected in the injector wells so that the injection fluids can sweep through the reservoir and mobilize/recover more oil. However, the formation of stable emulsion due to the residual chemicals in the recovered crude oil poses problems in the primary separation process. This study focuses on the effect of Alkali-Surfactant-Polymer injection on creaming and coalescence of crude oil and water emulsion produced in the primary gravity separator. In addition, a model is developed in this work to predict the separation effectiveness of oil and water emulsion containing ASP. In this work, the effect of ASP on the stability and the separation of crude oil water emulsion is investigated using the Turbiscan light transmission and back scattering device. The range of concentration of ASP used in this work is same as that found in the primary gravity separator. The investigation revealed that ASP plays an important role in the stability of the emulsion. A new model based on Stokes law and incorporating coalescence phenomena for the prediction of separation profile of oil and water in terms of separated layer profile is developed. The model is validated with experimental data obtained from the Turbiscan light transmission and backscattering device. The model is able to predict the separation profile of an oil-water emulsion containing ASP with excellent agreement. (a) Oil emulsion in a batch separator (b) Separation effectiveness of oil in water emulsion in terms creaming and coalescing interfaces in water height with time t.

Efficient separation of liquid-liquid mixture based on dispersion is often required in many industrial processes. Influence of mixing time, initial height of dispersion, mixing intensity and oil phase ratio on the separation time in liquid-liquid batch settler/separator were investigated in terms of coalescence and sedimentation profile. Experiments were carried out with oil in water type dispersion. Results showed that in most of cases coalescence controls the time of separation. The experiment results of liquid-liquid separation profiles were compared with the predefined model. MATLAB was used for the fitting of coalescence profile and calculating fitting constants. The data obtained from the batch experiments along with the mathematical model can be used as a good monitoring tool for studying the liquid-liquid dispersion behaviour in industrial units

Surface tension is an important property and its accurate prediction is required in most chemical engineering calculations. An equation for the surface tension of liquid hydrocarbons as a function of temperature is developed considering intermolecular attractions in liquid phase are reflected by its enthalpy. The model equation is validated with National Institute of Standards and Technology data on liquid hydrocarbon alkanes (C₁ to C₁₀ and C₁₂) accounting for molecular shape through sphericity factor. The molecular sphericity factor correlated well with molecular weight of the hydrocarbons. The accuracy of the proposed model equation is compared with the two empirical equations, and results showed that the proposed model equation predicts the surface tension of pure hydrocarbon liquids more accurately.

With the application of chemical enhanced oil recovery methods, water separation is a major issue in a production facility. Oil/water separation is suppressed with a stable emulsion. The present study evaluated the impact of different emulsifiers in enhanced oil recovery. The effectiveness of each additive such as an alkali, surfactant, or polymer on the stability of the emulsion was anticipated using laser scattering to measure the emulsion’s stability. An artificial neural network was applied to predict the effectiveness of the additives on stabilization/destabilization and to assess how alkali/surfactants, surfactant/polymers, and polymer/surfactants affect the separation profiles. Measurements of the viscosity and zeta potential of residual emulsion clarify that the increase in surfactant makes the emulsion stable and became unstable with the increase in the alkali and polymer. The droplet zeta potential was within −i9~−i5 mV. The absolute value of the zeta potential decreased at a high polymer concentration with a low surfactant concentration resulting in fast flocculence phenomena. With an increase in the surfactant concentration and the presence of high alkali, the droplet’s absolute zeta potential demonstrated an increase in the repulsion force in the emulsions. The study also focuses on the carbon number distribution, wax appearance temperature (WAT) and wax content of four crude oil samples from different field locations (Miri, Angsi, Penara and Dulang). Findings show that crude oil samples with higher mol percent of carbon distribution from C₂₀ to C₄₀ (paraffinic composition) contains higher wax content, wt% and subsequently results in higher wax appearance temperature (WAT). This is obviously shown by the crude oil sample from Penara field. Further similar investigation on other field locations will assist in characterizing the paraffinic composition in Malaysian oil basins.