Search

Miscible gas injection in tight/shale oil reservoirs presents a complex problem due to various factors, including the presence of a large number of nanopores in the rock structure and asphaltene and heavy components in crude oil. This method performs best when the gas injection pressure exceeds the minimum miscibility pressure (MMP). Accordingly, accurate calculation of the MMP is of special importance. A critical issue that needs to be considered is that the phase behavior of the fluid in confined nanopores is substantially different from that of conventional reservoirs. The confinement effect may significantly affect fluid properties, flow, and transport phenomena characteristics in pore space, e.g., considerably changing the critical properties and enhancing fluid adsorption on the pore wall. In this study, we have investigated the MMP between an asphaltenic crude oil and enriched natural gas using Peng-Robinson (PR) and cubic-plus-association (CPA) equations of state (EoSs) by considering the effect of confinement, adsorption, the shift of critical properties, and the presence of asphaltene. According to the best of our knowledge, this is the first time a model has been developed considering all these factors for use in porous media. We used the vanishing interfacial tension (VIT) method and slim tube test data to calculate the MMP and examined the effects of pore radius, type/composition of injected gas, and asphaltene type on the computed MMP. The results showed that the MMP increased with an increasing radius of up to 100 nm and then remained almost constant. This is while the gas enrichment reduced the MMP. Asphaltene presence changed the trend of IFT reduction and delayed the miscibility achievement so that it was about 61% different from the model without the asphaltene precipitation effect. However, the type of asphaltene had little impact on the MMP, and the controlling factor was the amount of asphaltene in the oil. Moreover, although cubic EoSs are particularly popular for their simplicity and accuracy in predicting the behavior of hydrocarbon fluids, the CPA EoS is more accurate for asphaltenic oils, especially when the operating pressure is within the asphaltene precipitation range. [ABSTRACT FROM AUTHOR]

The structural complexities of hydrocarbon reservoirs make it difficult to correlate geological and petrophysical properties. A successful field development depends on accurately mapping the spatial distributions of reservoir key parameters. In this study, we present results on rock type analysis, estimation, and uncertainty evaluation of geological and petrophysical data of 33 wells in one of the south Iranian gas fields. This paper is divided into two parts. In the first part, we used a reservoir rock classification theme based on identifying electrofacies (EFs) and hydraulic flow units by analyzing both log and core data. In the second part of the paper, we performed estimation, uncertainty evaluation, and assessment of the porosity–thickness relationship of the high-quality EFs using geostatistical techniques. We used sequential simulation schemes to map the spatial distributions of porosity and thickness of the high-quality EFs across the field. Using probabilistic approaches, the generated multiple realizations were used to quantify the local and spatial uncertainties. Variogram analysis showed that property distributions had a higher continuity and minimum variance in the NW–SE direction. Based on spatial uncertainty analysis, we found that the indicator-based sequential simulated maps exhibited less spatial uncertainty. Furthermore, the obtained probability maps indicated that the SE part of the study area is more suitable for drilling and production scenarios. [ABSTRACT FROM AUTHOR]

This study presents a novel approach for optimizing well paths in extended reach drilling (ERD) wells. Different trajectories can be used for ERD wells, each with its pros and cons. Previous research overlooked certain objective functions in single-objective optimization and lacked an autonomous method for selecting the best solution from Pareto optimal solutions in multi-objective optimizations. Furthermore, they lacked comparing different profiles in well design. Risk assessment and operational factors, which greatly influence optimization and drilling success, were insufficiently considered. This study utilized the multi-objective genetic algorithm (MOGA) and the technique for order preference by similarity to an ideal solution (TOPSIS) method to select the optimal well path based on torque, wellbore length, risk (e.g., keyseat), and required tools. First, all possible trajectories were determined, and MOGA identified the optimal path with minimal torque and length. The fuzzy decision-making method automatically selected the best solution from the Pareto optimal solution set. The associated risks and required tools are evaluated for each trajectory. Finally, the TOPSIS method selected the optimal trajectory based on torque, length, risks, and required tools. The case study demonstrated that the undersection path was the most advantageous trajectory for ERD wells, with a 60% closeness to the ideal state. The multiple build trajectory achieved 57% closeness, while the build and hold and double build paths had lower closeness values (43 and 28%, respectively). Consequently, it can be inferred that in the context of ERD wells, it is preferable to carry out the deviation process at deeper depths. [ABSTRACT FROM AUTHOR]

Reservoir characterization is one of the key stages in oil and gas exploration, appraisal, development, and optimal production. During the exploration phase, core analysis and well logging are basic information obtained after the pay zone identification. This study aims to integrate the concept of electrofacies (EFs) with hydraulic flow units (HFUs) to effectively characterize Permo–Triassic carbonate formations at the Kangan giant gas field in southwestern Iran. For this purpose, well log data collected from 10 drilled wellbores and 490 core data have been analyzed based on similar clusters and statistical features in which rock-type approaches were used in order to integrate petrophysical data with facies. In particular, we used a multi-resolution graph-based clustering (MRGC) method to determine EFs and the probability plot method, histogram analysis, and the plot of reservoir quality index versus the normalized porosity to identify HFUs. Based on the data, five electrofacies and six HFUs were identified. To establish a good connection between the electrofacies and the pay zones, we analyzed wells' cross sections to compare lithology, production potential in each zone, rock type, and amount of shale and eventually to adapt facies to flow units and determine the best quality reservoir zone. The main difference between this work and other studies in the literature is adopting a systematic approach based on integrating the geology (via EFs analysis) and engineering examination (via HFUs) to accurately characterize the hydrocarbon-bearing formations. The results of this study help in rapid and cost-effective carbonate reservoir characterization by combining electrofacies clustering and HFU analysis based on core and log data, which are available and routine information in all oil/gas fields. This, in turn, assists in developing the field, using more appropriate production and EOR scenarios, and even locating proper perforation sites. [ABSTRACT FROM AUTHOR]

Permeability is one of the key parameters in the characterization and modeling of the hydrocarbon reservoirs, which has an important effect on optimal production and field management. Permeability is directly obtained from the core data and indirectly from the well testing data. Determining permeability by using core is the most accurate and expensive method in the industry, also core information may only exist in some wells. Therefore, it is necessary to use methods that can determine permeability without core data. Use of hydraulic flow units (HFU) concept is one of the methods for estimate permeability. HFUs are a function of flow zone indicator (FZI), in which each flow unit has a unique FZI. Due to the fact that permeability estimation in wells without core data is one of the important issues in the oil industry, the purpose of this study is to estimate permeability in one of the southern Iranian carbonate gas fields. In this field, core data only exists in one of the wells; therefore, we used an integrated approach of HFUs and adaptive network fuzzy inference system (ANFIS) to estimate permeability. Subsequently, the values of FZI, RQI (reservoir quality index), and ϕZ (normalized porosity) were calculated for core samples, and six HFUs were identified by different methods which showed a high value of the correlation coefficient in each HFU. Based on HFU and FZI, permeability was estimated and compared with core permeability data. The average relative error between the core permeability and the estimated value is 1.83%. Eventually, based on conventional well log data and ANFIS, permeability values were estimated in un-cored wells. The average relative error between core permeability and ANFIS calculated has shown 5.21%. So it can be concluded that this method can be used in all un-cored wells by just using log data of high accuracy. [ABSTRACT FROM AUTHOR]

Pressure transient data from downhole gauges are one of the key parameters in characterizing reservoir properties and forecasting future reservoir performance. Reservoir pressure is usually measured under dynamic changes. The collected data usually contain different levels of noise, particularly due to imperfections in measuring instruments and imperfect calibrations. The latter is due to changes between the laboratory environment and reservoir conditions. To have accurate descriptions of reservoir, it is essential to smooth the pressure data. Most related studies have employed the wavelet transform to reduce noise. However, there appears to be little research addressing the use of other smoothing techniques for pressure transient data. This paper, therefore, evaluates and compares the performance of three types of smoothing and noise removal methods, namely wavelet transform as a widely used filtering technique, regression-based smoothers, and autoregressive smoothing methods to reduce artificial noise added to simulated dual-porosity pressure data. Particularly, noise is more pronounced in pressure derivative, and so denoising of pressure derivative requires more effective tools. The effectiveness of the noise removing methods was compared using mean square error. The results show that the regression-based methods lead to the same or even better reduction in the noise level as compared to the wavelet domain filter, while the employed autoregressive method results in a moderate performance. We also test the performance of various combinations of the different smoothing methods to filter the same noisy data. It is shown that the combined locally weighted scatterplot smooth (LOESS) and autoregressive moving average (ARMA) gives the best smoothing performance for pressure derivative data. Application of the combined LOESS-ARMA to real field data shows promising results. [ABSTRACT FROM AUTHOR]

The elastic modulus is one of the important parameters for analyzing the stability of engineering projects, especially dam sites. In the current study, the effect of physical properties, quartz, fragment, and feldspar percentages, and dynamic Young’s modulus (DYM) on the static Young’s modulus (SYM) of the various types of sandstones was assessed. These investigations were conducted through simple and multivariate regression, support vector regression, adaptive neuro-fuzzy inference system, and backpropagation multilayer perceptron. The XRD and thin section results showed that the studied samples were classified as arenite, litharenite, and feldspathic litharenite. The low resistance of the arenite type is mainly due to the presence of sulfate cement, clay minerals, high porosity, and carbonate fragments in this type. Examining the fracture patterns of these sandstones in different resistance ranges showed that at low values of resistance, the fracture pattern is mainly of simple shear type, which changes to multiple extension types with increasing compressive strength. Among the influencing factors, the percentage of quartz has the greatest effect on SYM. A comparison of the methods' performance based on CPM and error values in estimating SYM revealed that SVR (R2 = 0.98, RMSE = 0.11GPa, CPM =  + 1.84) outperformed other methods in terms of accuracy. The average difference between predicted SYM using intelligent methods and measured SYM value was less than 0.05% which indicates the efficiency of the used methods in estimating SYM. [ABSTRACT FROM AUTHOR]