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As oil and gas exploration progressively moves into deepwater regions, flow assurance in multiphase transport pipelines has become a critical issue. The complex conditions in deepwater environments often lead to the formation of hydrates, which can block pipelines. Therefore, the injection of hydrate inhibitors has become a common practice. In engineering practice, conservative dosing rates are typically adopted to ensure safety margins, but this often leads to a significant increase in costs. This paper presents a steady-state computational model to determine the distribution of inhibitors along the pipeline. The innovation lies in combining multiphase flash calculations with pipeline flow modeling, offering high accuracy and a wide range of applicability. This model is designed with modular flexibility, allowing substitution of different steady-state flow models and thermodynamic models, thereby extending its adaptability to diverse engineering scenarios. The computational results are compared with those from OLGA (a commercial software for multiphase flow simulation), and the results demonstrate that the proposed method achieves high accuracy, indicating that it can serve as a reference for optimizing inhibitor dosage.

The thermodynamic properties of fluid mixtures play a crucial role in designing physically meaningful models and robust algorithms for simulating multi-component multi-phase flow in subsurface, which is needed for many subsurface applications. In this context, the equation-of-state-based flash calculation used to predict the equilibrium properties of each phase for a given fluid mixture going through phase splitting is a crucial component, and often a bottleneck, of multi-phase flow simulations. In this paper, a capillarity-wise Thermodynamics-Informed Neural Network is developed for the first time to propose a fast, accurate and robust approach calculating phase equilibrium properties for unconventional reservoirs. The trained model performs well in both phase stability tests and phase splitting calculations in a large range of reservoir conditions, which enables further multi-component multi-phase flow simulations with a strong thermodynamic basis.

A computational study on thermal stability was conducted the first time, combining the modified quasi-chemical model, the Antoine equation, and the adiabatic flash evaporation calculation principle to design a method to calculate the system pressure-temperature (P-T) phase diagram of binary chloride molten salts. The evaporation temperature of the molten salt obtained by analyzing the P-T phase diagram of the eutectic molten salt clearly defined the upper limit of the optimal operating temperature of the mixed molten salt. The results indicated that the upper-temperature limits of NaCl-KCl, NaCl-CaCl2, KCl-CaCl2, NaCl-MgCl2, and KCl-MgCl2 are determined to be 1141 K, 1151 K, 1176 K, 1086 K, and 1068 K. The maximum working temperature was measured experimentally using a thermogravimetric analysis (TGA), and the relative error between the calculation and experiment was calculated. The maximum error between the calculated and experimental values of the maximum operating temperature was 6.02%, while the minimum was 1.29%, demonstrating the method’s high accuracy. Combined with the lowest eutectic temperature and the upper-temperature limits of binary chloride molten salts, the stable operating temperature ranges of NaCl-KCl, NaCl-CaCl2, KCl-CaCl2, NaCl-MgCl2, and KCl-MgCl2 are 891~1141 K, 750~1151 K, 874~1176 K, 732~1086 K, and 696~1086 K.

In recent years, CO2 flooding has become an important technical measure for oil and gas field enterprises to further improve oil and gas recovery and achieve the goal of “dual carbon”. It is also one of the concrete application forms of CCUS. Numerical simulation based on CO2-EOR plays an indispensable role in the study of the mechanism of CO2 flooding and buried percolation, allowing for technical indicators to be selected and EOR/EGR prediction to be improved for reservoir engineers. This paper discusses the numerical simulation techniques related to CO2 flooding and storage, including mathematical models and solving algorithms. A multiphase and multicomponent mathematical model is developed to describe the flow mechanism of hydrocarbon components–CO2–water underground and to simulate the phase diagram of the components. The two-phase P-T flash calculation with SSI (+DEM) and the Newton method is adopted to obtain the gas–liquid phase equilibrium parameters. The extreme value judgment of the TPD function is used to form the phase stability test and miscibility identification model. A tailor-made multistage preconditioner is built to solve the linear equation of the strong-coupled, multiphase, multicomponent reservoir simulation, which includes the variables of pressure, saturation, and composition. The multistage preconditioner improves the computational efficiency significantly. A numerical simulation of CO2 injection in a carbonate reservoir in the Middle East shows that it is effective for researching the recovery factor and storage quantity of CO2 flooding based on the above numerical simulation techniques.

The objective of this study was to evaluate the capability of machine learning models to accurately predict complex near-critical phase behavior in CO2–hydrocarbon systems, which are crucial for enhanced oil recovery and carbon storage applications. We compared the physical Peng–Robinson equation of state model to machine learning algorithms under varying temperatures, pressures, and composition, including challenging near-critical scenarios. We used a direct neural network model and two hybrid model approaches to capture physical behavior in comprehensive compositional space. While all the models showed great performance during training and validation, the Direct Model exhibited unphysical behavior in compositional space, such as fluctuations in equilibrium constants and tie-line crossing. Hybrid Model 1, integrating a single Rachford–Rice iteration for physical constraints, showed an improved consistency in phase predictions. Hybrid Model 2, utilizing logarithmic transformations to better handle nonlinearities in equilibrium constants, further enhanced the accuracy and provided smoother predictions, particularly in the near-critical region. Overall, the hybrid models demonstrated a superior ability to balance computational efficiency and physical accuracy, closely aligning with the reference of the Peng–Robinson equation of state. This study highlights the importance of incorporating physical constraints into machine learning models for reliable phase behavior predictions, especially under near-critical conditions.

Recent developments in predicting microemulsion phase behavior for use in chemical flooding are based on the hydrophilic-lipophilic deviation (HLD) and net-average curvature (NAC) equation-of-state (EoS). The most advanced version of the HLD-NAC EoS assumes that the three-phase micelle characteristic length is constant as parameters like salinity and temperature vary. In this paper, we relax this assumption to improve the accuracy and thermodynamic consistency of these flash calculations. We introduce a variable characteristic length in the three-phase region based on experimental data that is monotonic with salinity or other formulation variables, such as temperature and pressure. The characteristic length at the boundary of the three-phase region is then used for flash calculations in the two-phase lobes for Winsor type I/II. The functional form of the characteristic length is made consistent with the Gibbs phase rule. The improved EoS can capture asymmetric phase behavior data around the optimum, whereas current HLD-NAC based models cannot. The variable characteristic length formulation also resolves the thermodynamic inconsistency of existing phase behavior models that give multiple solutions for the optimum. We show from experimental data and theory that the inverse of the characteristic length varies linearly with formulation variables. This important result means that it is easy to predict the characteristic length in the three-phase region, which also improves the estimation of surrounding two-phase lobes. The results show that the optimum solubilization ratio can change significantly by a factor of two when variable characteristic length is included as temperature and pressure change. This can in turn greatly impact the interfacial tension (IFT) at optimum. This improved physical understanding of microemulsion phase behavior should aid in the design of surfactant blends and improve recovery predictions in a chemical flooding simulator.

In multiphase (≥3) equilibrium calculations, when the Newton method is used to solve the material balance (Rachford-Rice) equations, poorly conditioned Jacobian can lead to false convergence. We present a robust successive substitution method that solves the multiphase Rachford-Rice equations sequentially using the method of bi-section while considering the monotonicity of the equations and the locations of singular hyperplanes. Although this method is slower than Newton solution, as it does not rely on Jacobians that can become poorly conditioned, it can be inserted into Newton iterations upon the detection of a poorly conditioned Jacobian. Testing shows that embedded successive substitution steps effectively improved the robustness. The benefit of the Newton method in the speed of convergence is maintained.

For some of the EOS models the dimension of equilibrium problem can be reduced. Stability and difficulties in implementation are among the problems of flash calculation. In this work, a new reduction technique is presented to prepare a reduced number of equilibrium equations. Afterwards, a number of appropriate solution variables are selected for the prepared equation system to solve the equations in an efficient numerical scheme. All the derivatives and solution procedures for the new reduced flash calculation framework were prepared based on Peng-Robinson equation of state. One reservoir oil sample and one gas condensate sample were selected from published literature to evaluate the proposed method for the calculations of reservoir fluids equilibrium. The equilibrium calculations with the proposed reduction technique were compared to full flash calculations. The reduced formulation implementation is simple and straightforward as it is derived from full flash fugacity equality criteria. The presented technique not only reduces the number of equations, and hence simplifies flash problem, but also presents a comparable convergence behavior and offers the same solution system for different reservoir fluid types. The results, demonstrates the proposed method performance and the accuracy for modeling with complex equilibrium calculations like compositional reservoir simulation when there are many components available in the mixture fluid description.

We derive a Eulerian--Lagrangian formulation for multicomponent multiphase compositional flow and transport in porous media, by utilizing momentum balance to define a barycentric component velocity for each component based on phase velocities and the relative presence of the component in each phase. This weighted velocity obviates the need for tracking multiple phases without losing the ability to handle the nonlinear complexity of the problem. The formulation can be combined with different types of pressure solvers in solving the coupled systems of compositional models. A full thermodynamic flash calculation is carried out to determine phase stability and composition. Numerical experiments are presented to investigate the performance of the method, compared with widely used explicit and implicit upwind methods in the context of a two-phase, three component compositional flow and transport process. These results demonstrate that the Eulerian--Lagrangian formulation not only represents the solutions properly, but also has obvious computational benefits compared to the widely used traditional explicit and implicit upwind methods. [PUBLICATION ABSTRACT]

We briefly review methods for determining minimum miscibility pressure (MMP). We propose a modified multiple mixing cell method for determining the minimum miscibility pressure, based on the variation in the tie-lines at different pressures. First using the conventional multiple mixing cell method, we calculated the tie-line length, studied the tie-line pattern, and found the pressure-dependent critical points. Then we proposed some steps to improve the existing algorithm. The results obtained using the modified method are compared with the results of other methods. We have established that our algorithm can solve the problem of the complexity of searching for key tie lines, as is done in the calculation by the conventional mixing cell method, and thus our algorithm ensures that we find an accurate value of the MMP.