The Influence of Multiphase Flow on the Interpretation of Pressure Transient Data in Multilayer Reservoirs With Cross Flow

The authors investigated multiphase flow effects on the analysis of pressure responses in layered reservoirs with cross flow. Virtually all studies on the subject of multiphase well test analysis have been carried out in single-layer reservoirs. However, many reservoirs are found to be composed of number of layers whose characteristics are different from each other and the wells in such reservoirs may be completed and produced from more than one layer. In order to examine the behavior of pressure data in multiphase multilayer reservoirs with interlayer communication several cases are considered. The contrast in phase saturations in each layer is the parameter of interest. For each case the applicability of conventional well test analysis is investigated and reservoir parameters such as phase mobilities, skin factor, and average reservoir pressure are compared with actual values. According to the results of this work, conventional multiphase well test analysis in the single layer reservoir can be used in layered reservoirs with cross flow; however, the data should be interpreted with care if horizontal saturation gradient in the layers is significant.     

Utilization of produced gas of CO2 flooding to improve oil recovery

Large amounts of mixed gas containing CO₂ and hydrocarbon would be produced during CO₂ flooding. Injecting the produced gas back to reservoir can not only make full use of CO₂, but also can reduce air contamination. Taking produced crude oil and gases with different CO₂ concentration from Jilin oilfield as examples in this paper, the phase behavior and the physical properties of live oil-gas system were measured with a visible PVT apparatus. In terms of oil viscosity reduction and swelling, the gas with high CO₂ concentration was found to be substantially effective. Furthermore, comparative slim tube tests of the oil recovery performance using the five kinds of gases under different operating pressure were conducted in one-dimensional model. Results indicate that displacement efficiency increases linearly with the increasing CO₂ content in the recycle gas; displacement efficiency increases with operating pressure under immiscible conditions. Re-injecting produced gas with relative high CO₂ concentration back to reservoir is a method both time-saving and cost-effective.     

How much would silica nanoparticles enhance the performance of low-salinity water flooding?

Nanofluids and low-salinity water(LSW)flooding are two novel techniques for enhanced oil recovery.Despite some efforts on investigating benefits of each method,the pros and cons of their combined application need to be evaluated.This work sheds light on performance of LSW augmented with nanoparticles through examining wettability alteration and the amount of incremental oil recovery during the displacement process.To this end,nanofluids were prepared by dispersing silica nanoparticles(0.1 wt%,0.25 wt%,0.5 wt% and 0.75 wt%)in 2,10,20 and 100 times diluted samples of Persian Gulf seawater.Contact angle measurements revealed a crucial role of temperature,where no wettability alteration occurred up to 80 ℃.Also,an optimum wettability state(with contact angle 22°)was detected with a 20 times diluted sample of seawater augmented with 0.25 wt% silica nanoparticles.Also,extreme dilution(herein 100 times)will be of no significance.Throughout micromodel flooding,it was found that in an oil-wet condition,a combination of silica nanoparticles dispersed in 20 times diluted brine had the highest displacement efficiency compared to silica nanofluids prepared with deionized water.Finally,by comparing oil recoveries in both water-and oil-wet micromodels,it was concluded that nanoparticles could enhance applicability of LSW via strengthening wettability alteration toward a favorable state and improving the sweep efficiency.     

Developing a simple-to-use predictive model for prediction of hydrate formation temperature

Hydrate formation may be a common occurrence during oil and gas drilling and production operation when temperature of these solid crystalline compounds that formed in the presence of free water decreases at elevated pressure. Also, they have often been found responsible for operating difficulties at wellheads, pipelines and other processing equipment. Nowadays, because of the importance of predicting hydrate formation condition, different accurate methods have been used. Besides the experiential correlations that are common for predicting, the developments in the field of modelling led to the use of different methods in a thermodynamic way. In fact, because of the risk of experimental uncertainties and to remove the need for intricate analytic equations and empirical correlations, the computational intelligence model, which result in the lowest error and based on experimental data, is strongly proposed, in attempts to solve complex industrial problems. In this article, in order to predict gas hydrate formation condition, two smart techniques are established based on feed-forward neural network (artificial neural network (ANN)) which is optimised by imperialist competitive algorithm (ICA). The ICA-ANN model is conducted utilising the empirical data released in the literature and finally the performance of ICA-ANN model is compared with the conventional ANN model. Furthermore, they have been compared with an accurate thermodynamic model at different operating conditions. The outcomes, contrary to expectations, establish that the ICA-ANN model has poor performance when compared with the ANN.     

Evolving simple-to-apply models for estimating thermal conductivity of supercritical CO2

Today, due to extensive applications of supercritical fluids technology in various chemical engineering process and industrial fields, predicting thermal conductivity of supercritical carbon dioxide is vital. In this research, two simple-to-apply models have been developed to estimate thermal conductivity of supercritical CO₂ as a function of temperature, pressure and density over broad ranges. This research presents a predictive tool based on LSSVM to predict thermal conductivity of supercritical CO₂. Genetic algorithm is employed to determine hyper-variables which are included in the LSSVM approach. In this regard, a set of accessible data containing 745 data points has been gathered from the previous published papers. Estimations are found to be in excellent agreement with reported data. Moreover, statistical analyses have been applied to evaluate the performance of two models. The obtained values of Mean Squared Error and R-Square were 7.415866, 0.9935 and 0.046527, 1.00 for the correlation and LSSVM model, respectively. The developed tools can be of immense practical value for chemical engineers to have a quick check of thermal conductivity of supercritical CO₂ at an extensive range of conditions.     

Hydrogen purification layered bed optimization based on artificial neural network prediction of breakthrough curves

Artificial neural network has generally been used for a quantity of tasks such as classification, prediction, clustering and association analysis in different application fields. To the best of our knowledge, there are few researches on breakthrough curve used artificial neural network. In this paper, an artificial neural network model is established for breakthrough curves prediction in relation to a ternary components gas with a two-layered adsorbent bed piled up with activated carbon (AC) and zeolite, and an optimization is concluded by the artificial neural network. The performance data which acquired by Aspen model has been utilized for training artificial neural network (ANN) model. The ANN model trained has great competence for making prediction of hydrogen purification performance of PSA cycle with impressive speed and rational accuracy. On the strength of the ANN model, we implemented an optimization for seeking first-rank PSA cycle parameters. The optimization is concentrated on the effect of inlet flow rate, pressure and layer ratio of activated carbon height to zeolite height. Furthermore, this paper shows that the PSA cycle's optimal operation parameters can be obtained by use of ANN model and optimization algorithm, the ANN model has been trained according to the data generated by Aspen adsorption model.     

Canada to move out of Kyoto agreement…

The Canadian government, under pressure from energy producers worried about the costs of the Kyoto climate change protocol, has given a clear sign that it might not ratify the Kyoto treaty.     

Experimental investigation and modeling hybrid nano-porous membrane process for industrial oily wastewater treatment

The aims of this work are to construct a pilot scale purification set-up using membrane process for Tehran Oil Refining Company desalter plant wastewater. The investigation was shown that the high amount of impurities in the feed was the main reason of low permeation flux. The nano-porous membrane-powdered activated carbon (NPM–PAC) was employed to settle this problem. Results demonstrated NPM alone was ineffective in removing TSS, COD, and TOC. In the NPM process the removal of COD and TOC are around 62.5 and 75.1%, respectively, and the steady permeation flux (SPF) is around 78.7 L/(m² h). Optimum PAC dosage, which leads to less deposit layer with high porosity on the membrane surface, could increase permeation flux up to 133.8 L/(m² h), the removal of COD and TOC, 78.1% and 90.4%, respectively, and also decreased steady fouling resistance (SFR) around 46.1%. Hermia's models were employed to investigate mechanism of preventing membrane fouling. After coagulation, the kinetic constants, K_b, K_i, K_s, and K_c, showed lower amounts when NPM filtration used alone. Thus, a NPM–PAC hybrid membrane system has the potential to be an effective method to improve NPM removal efficiency in high percentages as well as to improve membrane fouling and permeation flux in desalter plant.     

A developed smart technique to predict minimum miscible pressure—eor implications

Miscible gas injection (MGI) processes such as miscible CO₂ flooding have been in use as attractive EOR options, especially in conventional oil reserves. Optimal design of MGI is strongly dependent on parameters such as gas–oil minimum miscibility pressure (MMP), which is normally determined through expensive and time‐consuming laboratory tests. Thus, developing a fast and reliable technique to predict gas–oil MMP is inevitable. To address this issue, a smart model is developed in this paper to forecast gas–oil MMP on the basis of a feed‐forward artificial neural network (FF‐ANN) combined with particle swarm optimisation (PSO). The MMP of a reservoir fluid was considered as a function of reservoir temperature and the compositions of oil and injected gas in the proposed model. Results of this study indicate that reservoir temperature among the input parameters selected for the PSO–ANN has the greatest impact on MMP value. The developed PSO–ANN model was examined using experimental data, and a reasonable match was attained showing a good potential for the proposed predictive tools in estimation of gas–oil MMP. Compared with other available methods, the proposed model is capable of forecasting oil–gas MMP more accurately in wide ranges of thermodynamic and process conditions. All predictive models used other than the PSO–ANN model failed in providing a good estimate of the oil–gas MMP of the hydrocarbon mixtures in Azadegan oilfield, Iran. © 2013 Canadian Society for Chemical Engineering     

Economic evaluation of natural gas transportation from Iran’s South-Pars gas field to market

The worldwide consumption of natural gas is rapidly increasing. To satisfy such a demand, there are some plans to transport natural gas from South-Pars gas field, the largest natural gas field of Iran, to some energy consuming countries. There are several possible technologies for transporting gas from production fields to consuming markets as gas, including PNG (pipeline natural gas), LNG (liquefied natural gas), CNG (compressed natural gas) and NGH (natural gas hydrate). Gas transmission projects are sensitive to technology selection and depending on the capacity and distance; chosen technology may affect the economy of the entire project noticeably. In this work, transporting 100 × 10⁶ standard m³/d natural gas from port of Assaluyeh in south of Iran to potential markets using alternative technologies such as PNG, LNG, CNG and NGH has been investigated. To do such a study, required processes for converting natural gas to desired product and then transporting it to market have been designed and using an economical model, cost of transporting natural gas as a function of distance, has been estimated. Results show for the investigated case, PNG has the lowest production cost for distances up to 7600 km and for larger distances, LNG has the lowest production cost.