In this paper, a very efficient method, called single matrix block analyzer (SMBA), has been developed to determine relative permeability and capillary pressure curves from spontaneous imbibition (SI) data. SMBA mimics realistically the SI tests by appropriate boundary conditions modeling. In the proposed method, a cuboid with an identical core plug height is considered. The equal dimensions of the cuboid in x and y directions are set such that the cylindrical core plug and the cuboid have the same shape factor. Thus, by avoiding the difficulties of the cylindrical coordinates, a representative model for the core plug is established. Appropriate grid numbers in x-y and z directions are specified to the model. Furthermore, the rock and fluid properties of SI test are set in the SMBA. By supposing forms of the oil-water capillary pressure and relative permeability and comparing the oil recovery curves of SMBA and SI data, capillary pressure and relative permeability can be determined. The SMBA is demonstrated using three experimental data with different aging times. Suitable equations are employed to represent the capillary pressure and relative permeability curves. The genetic algorithm is used as the optimization tool. The obtained results, especially for capillary pressure, are in good agreement with the experimental data. Moreover, the Bayesian credible interval (P10 and P90) evaluated by the Neighborhood Bayes Algorithm (NAB) is quite satisfactory.

Klinkenberg permeability is an important parameter in tight gas reservoirs. There are conventional methods for determining it, but these methods depend on core permeability. Cores are few in number, but well logs are usually accessible for all wells and provide continuous information. In this regard, regression methods have been used to achieve reliable relations between log readings and Klinkenberg permeability. In this work, multiple linear regression, tree boost, general regression neural network, and support vector machines have been used to predict the Klinkenberg permeability of Mesaverde tight gas sandstones located in Washakie basin. The results show that all the four methods have the acceptable capability to predict Klinkenberg permeability, but support vector machine models exhibit better results. The errors of models were measured by calculating three error indexes, namely the correlation coefficient, the average absolute error, and the standard error of the mean. The analyses of errors show that support vector machine models perform better than the other models, but there are some exceptions. Support vector machine is a relatively new intelligence method with great capabilities in regression and classification tasks. Herein, support vector machine was used to predict the Klinkenberg permeability of a tight gas reservoir and the performances of four regression techniques were compared.

Pore pressureis defined as the pressure of the fluid inside the pore space of the formation, which is also known as the formation pressure. When the pore pressure is higher than hydrostatic pressure, it is referred to as overpressure. Knowledge of this pressure is essential for cost-effective drilling, safe well planning, and efficient reservoir modeling. The main objective of this study is to estimate the formation pore pressure as a reliable mud weight pressure using well log data at one of oil fields in the south of Iran. To obtain this goal, the formation pore pressure is estimated from well logging data by applying Eaton’s prediction method with some modifications. In this way, sonic transient time trend line is separated by lithology changes and recalibrated by Weakley’s approach. The created sonic transient time is used to create an overlay pore pressure based on Eaton’s method and is led to pore pressure determination. The results are compared with the pore pressure estimated from commonly used methods such as Eaton’s and Bowers’s methods. The determined pore pressure from Weakley’s approach shows some improvements in comparison with Eaton’s method. However, the results of Bowers’s method, in comparison with the other two methods, show relatively better agreement with the mud weight pressure values.

Solubility of hydrocarbons in water is important due to ecological concerns and new restrictions on the existence of organic pollutants in water streams. Also, the creation of a thermodynamic model has required an advanced study of the phase equilibrium between water (as a basis for the widest spread muds and amines) and gas hydrocarbon phases in wide temperature and pressure ranges. Therefore, it is of great interest to develop semi-empirical correlations, charts, or thermodynamic models for estimating the solubility of hydrocarbons in liquid water. In this work, a thermodynamic model based on Mathias modification of Sova-Redlich-Kwong (SRK) equation of state is suggested using classical mixing rules with new binary interaction parameters which were used for two-component systems of hydrocarbons and water. Finally, the model results and their deviations in comparison with the experimental data are presented; these deviations were equal to 5.27, 6.06, and 4.1% for methane, ethane, and propane respectively.

A value chain is a series of events that takes a raw material and with each step adds value to it. Global interest in the application of natural gas (NG) in production and transportation has grown dramatically, representing a long-term, low-cost, domestic, and secure alternative to petroleum-based fuels. Many technological solutions are currently considered on the market or in development, which address the challenge and opportunity of NG. In this paper, a decision support system (DSS) is introduced for selecting the best fuel to develop in the value chain of NG through four options, namely compressed NG (CNG), liquefied NG (LNG), dimethyl ether (DME), and gas-to-liquids (GTL). The DSS includes a model which uses the technique for order performance by similarity to ideal solution (TOPSIS) to select the best fuel in the value chain of NG based on the attributes such as market situations, technology availability, and transportation infrastructure. The model recommends some key guidelines for two branches of countries, i.e. those which have NG resources and the others. We believe that applying the proposed DSS helps the oil and gas/energy ministries in a most effective and productive manner dealing with the complicated fuel-related production and transportation decision-making situations.

Among the all parameters affecting the performance of a downhole de-oiling hydrocyclone, the investigation of internal flow field deserves more attempts especially in the petroleum industry. In this study, the effects of inlet flow rate, inlet oil volume fraction, and oil droplet diameter on the separation efficiency and pressure drop ratio have been investigated along the hydrocyclone body. All the simulations were performed using computational fluid dynamics (CFD) techniques, in which the Eulerian multiphase model and the Reynolds stress turbulent model were employed for the prediction of multiphase and turbulent flow parameters through the hydrocyclone. The velocity component profiles, separation efficiency, pressure drop, and volume fraction are also other parameters which have been considered in this work. The results of the simulations illustrate good agreement with the reported experimental data. Furthermore, the simulations indicate that the separation efficiency almost increases twofold, when the droplet diameter increases from 25 to 50 micron. The effect of inlet flow rate on the separation efficiency is so significant that an increase in inlet flow rate from 5 to 25 l/min causes a sharp increase in the separation efficiency and raises it 2.5 times the initial value. However, the inlet oil volume fraction showed a minor effect on the hydrodynamic flow behavior in the hydrocyclone body compared to the other investigated parameters.