Iranian Journal of Oil & Gas Science and Technology
Volume:7 Issue: 1, Winter 2018

In this paper, the potentials of using particles, especially nanoparticles, in enhanced oil recovery is investigated. The effect of different nanoparticles on wettability alteration, which is an important method to increase oil recovery from oil-wet reservoirs, is reviewed. The effect of different kinds of particles, namely solid inorganic particles, hydrophilic or hydrophobic nanoparticles, and amphiphilic nanohybrids on emulsion formation (which is cited as a contributing factor in crude oil recovery) and emulsion stability is described. The potential of nanohybrids for simultaneously acting as emulsion stabilizers and transporters for catalytic species of in situ reactions in reservoirs is also reviewed. Finally, the application of nanoparticles in core flooding experiments is classified based on the dominant mechanism which causes an increase in oil recovery from cores. However, the preparation of homogeneous suspensions of nanoparticles is a technical challenge when using nanoparticles in enhanced oil recovery (EOR).
Future researches need to focus on finding out the proper functionalities of nanoparticles to improve their stability under harsh conditions of reservoirs.

The oilfield water in the Upper Sandstone Member of the Zubair reservoir (Barriemian-Hauterivian) at Rumaila North Oil Field was investigated for the interpretation of salinity and geochemical evolution of brine compositions. The interaction of the oilfield water with reservoir rock resulted in a brine water derived from the marine water origin of partial mixing with meteoric water similar to the compositional ranges of formation water from Gulf of Mexico offshore/onshore Mesozoic reservoirs. The high TDS (207350- 230100; average 215625 mg/L) is consistent with the electrical conductivity (340362-372762; average 351024μs), and predominantly represented by Cl (123679 mg/L) as anions and (29200 and 14674 mg/L) for Na and Ca as cations respectively. The contribution of cation (epm%) are as Na (70.2), Ca (18.9), Mg (8.1) and K (1.7); and anion as Cl (99.7), SO4 (0.25), HCO3 (0.07) and CO3 (0.005). sodium (57550-60500mg/L) is greater than of seawater six times, calcium and magnesium three times greater, and chloride 6.5 times greater, but Sulfate is depleted to six times less due to a sulfur release from sulphates and link with different hydrocarbon species, precipices as native sulphur and link with hydrogen forming H2S. The Zubair oilfield water is characterised by acidic pH (pH=5.2- 5.77) enhanced petrophysical properties, high specific gravity (1.228) predicts a high fluid pressure (4866 psi), hydrocarbon saturation (0.43%), water saturation (0.57%) and porosity (12.7). The Mineral saturation model indicates that the Zubair oilfield water is an unsaturated water with respect to all suggested minerals at 5.45, but at simulated pH, brucite being an equilibrium at pH 9.12, but brucite and portlandite being supersaturated at pH 11.9. The mineral solubility responses to the changes in temperature, pressure, pH, Eh, and ionic strength, thereby formation damage is proportionally developed.

Gas condensate reservoirs are characterized by a distinctive retrograde behavior and potential for condensate drop out during production and sampling. Efficient modeling of gas condensate reservoir requires careful phase behavior studies of samples collected prior to and during the production life of reservoir. In this work, an integrated characterization and tuning algorithm is proposed to analyze the pressure-volume-temperature (PVT) behavior of gas condensate samples. Each characterization and tuning scenario is described by a “path” which specifies the class of fluid, splitting and lumping (if any), the type of correlation, and grouping strategy (static or dynamic). Different characterization approaches were tested for the effective description of heavy end. Meanwhile, dynamic and static strategies were implemented to tune the equation of state (EOS) through non-linear regression. The optimum combination of characterization and tuning approach was explored for each sample by a rigorous analysis of the results. It was found out that the exponential distribution function gives the best performance for heavy end characterization in a dynamic tuning strategy. Also, analyses indicate that using higher single carbon number may not necessarily make EOS tuning more accurate. In addition, the optimum step is reached in either the third or fourth step for most cases in a dynamic tuning approach, and is sensitive neither to the characterization path nor to the selected end carbon number.

Reservoir characterization and asset management require comprehensive information about formation fluids. In fact, it is not possible to find accurate solutions to many petroleum engineering problems without having accurate pressure-volume-temperature (PVT) data. Traditionally, fluid information has been obtained by capturing samples and then by measuring the PVT properties in a laboratory. In recent years, neural network has been applied to a large number of petroleum engineering problems. In this paper, a multi-layer perception neural network and radial basis function network (both optimized by a genetic algorithm) were used to evaluate the dead oil viscosity of crude oil, and it was found out that the estimated dead oil viscosity by the multi-layer perception neural network was more accurate than the one obtained by radial basis function network.

Stripper columns are used for sweetening crude oil, and they must hold product hydrogen sulfide content as near the set points as possible in the faces of upsets. Since product quality cannot be measured easily and economically online, the control of product quality is often achieved by maintaining a suitable tray temperature near its set point. Tray temperature control method, however, is not a proper option for a multi-component stripping column because the tray temperature does not correspond exactly to the product composition. To overcome this problem, secondary measurements can be used to infer the product quality and adjust the values of the manipulated variables. In this paper, we have used a novel inferential control approach base on adaptive network fuzzy inference system (ANFIS) for stripping process. ANFIS with different learning algorithms is used for modeling the process and building a composition estimator to estimate the composition of the bottom product. The developed estimator is tested, and the results show that the predictions made by ANFIS structure are in good agreement with the results of simulation by ASPEN HYSYS process simulation package. In addition, inferential control by the implementation of ANFIS-based online composition estimator in a cascade control scheme is superior to traditional tray temperature control method based on less integral time absolute error and low duty consumption in reboiler.

Heat EXchangers (HEX) that are used in City Gate Station (CGS) systems are modeled numerically to recover the exhaust waste heat. It was tried to find the best viscous model to obtain results in accordance with experimental results and to change the heat exchanger design. This HEX is used for recovering heat from exhaust flue gas with a mixture of 40% water and 60% ethylene glycol as the cooling fluid. Then, the effects of sizes and numbers of fins and tube rows on recovered heat rate were investigated under various pump speeds. As the first step in solving the problem, SST k–ω and RNG k–ε suitable viscous models were chosen for these kinds of problems. Secondly, a new HEX is designed at a fixed coolant speed, pipe and fin thickness, and shell dimension because of operational constraints. Finally, the best HEX with the minimum pressure drop (minimum fin number) is numerically analyzed, and the new HEX specifications were extracted.