Gas Analysis by In Situ Combustion in Heavy‐Oil Recovery Process: Experimental and Modeling Studies

Enormous efforts have been made to facilitate produced‐gas analyses by in situ combustion implication in heavy‐oil recovery processes. Robust intelligence‐based approaches such as artificial neural network (ANN) and hybrid methods were accomplished to monitor CO₂/O₂/CO. Implemented optimization approaches like particle swarm optimization (PSO) and hybrid approach focused on pinpointing accurate interconnection weights through the proposed ANN model. Solutions acquired from the developed approaches were compared with the pertinent experimental in situ combustion data samples. Implication of hybrid genetic algorithm and PSO in gas analysis estimation can lead to more reliable in situ combustion quality predictions, simulation design, and further plans of heavy‐oil recovery methods.     

An Integrated Reservoir Characterization Analysis in a Carbonate Reservoir: A Case Study

Abstract

Reservoir characterization is one of the most challenging subjects in carbonate reservoirs. In this study flow zone index (FZI), Winland, and initial water saturation methods were used to classify rock typing in an Iranian oil field. In addition, stratigraphic modified Lorenz plots were generated for the purpose of identifying the flow zone and barriers in each well. The results were consistent with Winland result and FZI. The scanning electron microscopy photomicrographs, pore throat radius, grain size distribution data, and thin section of the obtained rock type were studied and found to be consistent with the findings of this work.     

Adsorptive stripping voltammetric determination of podophyllotoxin,an antitumour herbal drug,at multi-walled carbon nanotube paste electrode

Adsorption stripping voltammetry, a very sensitive electroanalytical method, was employed to determine podophyllotoxin, a kind of antitumour herbal drug at a multi-wall carbon nanotube (MWCNT)-modified carbon paste electrode (CPE) surface. In the following anodic sweep from 0.5 to 1.5 V, podophyllotoxin, adsorbed at the MWCNT-modified CPE surface, was oxidized and yielded a sensitive oxidation peak with E _1/2/E _p approximately 1.16 V/1.18 V over the scan rates of 10–120 mV s⁻¹. From CV and SWV studies of podophyllotoxin in the acetate buffers of various pH values, it was found that protons were involved in the oxidation of the drug at the H⁺/e⁻ ratio of one (∆E _p/pH = 56 mV at 25 °C). Its electrochemical behaviour was irreversible. The experimental conditions, such as supporting electrolyte, pH value, accumulation time, ionic strength and scan rate, were optimized for the measurement of podophyllotoxin. The best results were obtained in 0.02 M acetate/acetic acid buffer (pH 4.6) containing 0.04 M KCl (1:49, v/v) for 60 s accumulation. The oxidation peak current varies linearly with the concentration of podophyllotoxin over the range of 199–1796 pg mL⁻¹. The limits of detection and quantification of the pure drug are 4.5 and 14.96 pg mL⁻¹, with the correlation coefficient, r = 0.998 and the relative standard deviation, RSD = 1.3% (n = 5). This new method was successfully applied to the determination of podophyllotoxin in a plant sample of the rhizome of Podophyllum hexandrum. Recoveries were 99.173–101.231%. The relative standard deviations of intraday and interday analyses for podophyllotoxin were 0.55 and 0.61%, respectively (n = 3).     

An Experimental Investigation of Asphaltene Precipitation During Natural Production of Heavy and Light Oil Reservoirs: The Role of Pressure and Temperature

Abstract

Many oil reservoirs encounter asphaltene precipitation as a major problem during natural production. In spite of numerous experimental studies, the effect of temperature on asphaltene precipitation during pressure depletion at reservoir conditions is still obscure in the literature. To study their asphaltene precipitation behavior at different temperatures, two Iranian light and heavy live oil samples were selected. First, different screening criteria were applied to evaluate asphaltene instability of the selected reservoirs using pressure, volume, and temperature data. Then, a high pressure, high temperature filtration (HPHT) setup was designed to investigate the asphaltene precipitation behavior of the crude samples throughout the pressure depletion process. The performed HPHT tests at different temperature levels provided valuable data and illuminated the role of temperature on precipitation. In the final stage, the obtained data were fed into a commercial simulator for modeling and predicting purposes of asphaltene precipitation at different conditions. The results of the instability analysis illustrated precipitation possibilities for both reservoirs which are in agreement with the oil field observations. It is observed from experimental results that by increasing the temperature, the amount of precipitated asphaltene in light oil will increase, although it decreases precipitation for the heavy crude. The role of temperature is shown to be more significant for the light crude and more illuminated at lower pressures for both crude oils. The results of thermodynamic modeling proved reliable applicability of the software for predicting asphaltene precipitation under pressure depletion conditions. This study attempts to reveal the complicated role of temperature changes on asphaltene precipitation behavior for different reservoir crudes during natural production.     

Introducing a New Method for Predicting PVT Properties of Iranian Crude Oils by Applying Artificial Neural Networks

Abstract

In this study artificial neural networks (ANNs) have been applied for the prediction of main pressure, volume, and temperature (PVT) properties, bubble point pressure (P_b), and bubble point oil formation volume factor (B_ob) of crude oil samples from different wells of Iranian oil reservoirs. Via a detailed comparison, the great power of ANNs with respect to traditional methods of predicting PVT properties, like Standing, Vasquez and Beggs, and Al-Marhoun, with higher prediction precision up to R² = 0.990 has been illustrated and the obtained parameters of ANNs for the application of prediction of other crude oil samples has been presented. The applied PVT data set in this study consists of 218 crude oil samples from Iranian reservoirs and for assurance of the applicability of the ANN model the PVT data set has been divided into 2 training (190 samples) and cross validation (28 samples) data sets and obtained ANNs from applying the training data set has been tested on the cross validation data set which has not been seen by the network during the training process. The obtained results for both training and cross validation data sets confirm the great prediction power of ANNs, for both data sets with respect to traditional PVT correlations.     

Intelligent Prediction of Porosity and Permeability from Well Logs for An Iranian Fractured Carbonate Reservoir

Abstract

One of the most important processes in reservoir engineering is reservoir characterization, in which the reservoir parameters such as porosity and permeability are calculated. These parameters have crucial importance in reservoir engineering computations like reserve estimates and reservoir management. Estimation of porosity and permeability from conventional well logs for uncored well intervals is a good suggestion, but the complexity of the fractured carbonate reservoir makes the application of traditional statistical models totally unreliable. In this article the power of the pattern recognition of artificial neural networks (ANNs) has been applied to develop a transformation map from available most related well logs to rock petrophysical properties of Darquvain reservoir in the southwest of Iran. Comparison of the obtained results illustrates that ANN models can yield more reliable results with respect to traditional models of estimating petrophysical properties. An ANN can be utilized as a flexible and powerful tool for reservoir characterization from available well logs in development projects in the oil and gas industry.     

Optimization Assisted Asphaltene Deposition Modeling in Porous Media During a Natural Depletion Scheme

Abstract

Changes in thermodynamic properties such as pressure, temperature, and composition may result in asphaltene precipitation and deposition in porous media. In addition, asphaltene deposition can cause wettability alteration, permeability reduction, and ultimately a decrease in the productivity of a reservoir. Natural depletion is one of the most common processes of asphaltene deposition in which pressure changes destabilize the dissolved asphaltene in the oil and settle them onto the rock surface.

In this work, natural depletion experiments in consolidated core samples were performed under simulated reservoir conditions to obtain reliable data and analyze the asphaltene deposition mechanisms.

A mass balance equation, momentum equation, asphaltene deposition, and permeability reduction models were applied to model the process of permeability changes as a result of asphaltene deposition. MATLAB programming language was used to calculate the numerical form of the above equations iteratively. A genetic algorithm technique was employed as the optimization tool for history matching and determination of model parameters.

Modeling and optimization results showed an accurate match with measured data. Optimization confirmed that all major deposition processes (surface deposition, entrainment, and pore throat plugging) were effective in permeability changes. Calculation of precipitated asphaltene saturation by the introduced equation provides information on the volume fraction of porous media that was evaded by the precipitated asphaltene particles.     

Monitoring the Effect of Discontinuous Shales on the Surfactant Flooding Performance in Heavy Oil Reservoirs Using 2D Glass Micromodels

Although most heavy oil reservoirs contain discontinuous shaly structures, there is a lack of fundamental understanding how the shaly structures affect the oil recovery efficiency, especially during surfactant flooding to heavy oils. Here, an experimental study was conducted to examine the effect of discontinuous shales on performance of surfactant flooding by introducing heterogeneities to represent streaks of shale in five-spot glass micromodels. Results show that oil recovery in presence of shale streak is lower than in its absence. Based on the authors’ observations, the presence of flow barriers causes premature breakthrough of injected fluids and also an unstable displacement front. As well, displacement efficiency of surfactant flooding is dependent strongly on the shale distribution configuration. Increasing shale content causes reduction of ultimate oil recovery and also severe fingering during water flooding while it compensates during surfactant flooding considerably. In shaly patterns, in the case of surfactant flooding, the oil recovery after breakthrough increases significantly, while it changes much less for the case of water flooding as well as flooding in homogeneous model. Oil recovery efficiency and breakthrough time improved with increasing surfactant concentration. However, beyond a specific limit of sodium dodecyl sulfate concentration, around 2000 ppm, incremental oil recovery becomes insignificant. Presence of connate water in surfactant flooding scheme can improve the recovery efficiency in shaly patterns. Results of this work can be helpful to investigate the optimal location of injection/production wells during enhanced oil recovery schemes in shaly reservoirs using five-spot micromodels.     

A posteriori error analysis of time-dependent Stokes problem by Chorin-Temam scheme

We present a posteriori-residual analysis for the approximate time-dependent Stokes model Chorin-Temam projection scheme (Chorin in Math. Comput. 23:341–353, 1969; Temam in Arch. Ration. Mech. Appl. 33:377–385, 1969). Based on the multi-step approach introduced in Bergam et al. (Math. Comput. 74(251):1117–1138, 2004), we derive error estimators, with respect to both time and space approximations, related to diffusive and incompressible parts of Stokes equations. Using a conforming finite element discretization, we prove the equivalence between error and estimators under specific conditions.