Log Permeability Research Articles

Determining Petrophysical Properties Using PLTs and Conventional Logs of the Middle Shale Member (4th Pay) of the Zubair Formation in the South Rumaila Oil Field, Southern Iraq

The petrophysical evaluation of the Zubair Formation's Middle Shale member (4th Pay) in the South Rumaila oil field has been conducted to characterize the reservoir and quantify its hydrocarbon prospects. Although the 4th Pay has a substantial oil reserve, it has never been studied. A gamma ray, resistivity, density, and neutron log were used to estimate the shale volume, porosity, water saturation, and hydrocarbon saturation for four oil-bearing subunits in the middle shale member (4th Pay), producing net pays of Middle Shale Member 7 (13 m), 10 (31 m), 20 (11 m) and 25 (16 m), respectively. Shale volume calculated using the Gamma-Ray Log method, averages between 0.05 % and 0.53 %. Effective porosity ranges from (0.08) % to (0.21) %, indicating good reservoir quality; permeability ranges from 0.9 to 193.6 mD, indicating well-sorted sands. Hydrocarbon saturation varies from 0.29 to 0.89, while water saturation ranges from (0.11) to (0.72). Production Log Tool and Nuclear Magnetic Resonance logs were used to estimate permeability. Production Logging Tool log permeability values ranged between 5.2 (Middle Shale Member 10, 3425 mD) and 49.7 (Middle Shale Member 25, 3448.5 mD), while Nuclear Magnetic Resonance log permeability values ranged between 5.81 (Middle Shale Member 10, 3425 mD and 49 (Middle Shale Member 25, 3448.5 mD). According to the correspondence between the two logs, the correspondence value equals 85%. Thus, the reservoir permeability can be calculated using the radial flow equation calculated from the Production Logging Tool log and generalized to areas where there is no core. Results show that permeability (K) is highest in subunits Middle Shale Member 20 and 25, which also have high porosity, low shale content, and fine grain sorting, making them key production subunits. Subunit Middle Shale Member, although lower in permeability, is the main reservoir with the most oil reserves. Subunits Middle Shale Member 5 and 7 have lower permeability due to high shale content and heterogeneity, with low porosity. Subunit Middle Shale Member 0 has very low permeability. The high oil shale content, due to its poor porosity and heterogeneity, renders it unproductive.

Study on the Dispersion Characteristics of Coal Complex Resistivity under the Effect of Liquid Nitrogen Cyclic Freeze-Thaw and Evaluation of Permeability Enhancement.

The effect of liquid nitrogen freeze-thaw fracturing on coal seams can be potentially evaluated by the complex resistivity method. The real part (Reρ) and the imaginary part (Imρ) of the complex resistivity and permeability of coal were determined under different cycle times and in different bedding directions. The reason for permeability enhancement was discussed, and the dispersion mechanism of complex resistivity during cyclic freeze-thaw fracturing was analyzed. The results indicated that (1) the complex resistivity parameters have a good response to the cycle times; Reρ, |Imρ|, and the dispersion degree (α) are positively correlated with cycle time; the fully polarized frequency (f p) of Reρ, the characteristic frequency (f c) of Imρ, and variation are negatively correlated with cycle time. (2) The difference in complex resistivity parameters between the vertical bedding direction and the parallel bedding direction is significant, and the difference in electrical properties of the bedding structure continuously decreases with the increase in cycle time. (3) Under the effect of liquid nitrogen cyclic freeze-thaw, a complex network of fractures in coal is formed, the anisotropic characteristics of coal are weakened, and effective conductive channels are damaged. The peak frost heave force decreases exponentially with the increase in cycle time, and the difference in bedding electrical properties gradually disappears. (4) Comparing the inversion degree of measured data with three conductive models, ρ0 and τ are selected as the optimum parameters for evaluating the effect of liquid nitrogen cyclic freeze-thaw. A logarithmic permeability evaluation model is constructed based on ρ0 and τ. This work provides a new perspective based on electrical detection for evaluating the permeability enhancement of coal during liquid nitrogen cyclic freeze-thaw.

Improving permeability prediction via Machine Learning in a heterogeneous carbonate reservoir: application to Middle Miocene Nullipore, Ras Fanar field, Gulf of Suez, Egypt

Predicting and interpolating the permeability between wells to obtain the 3D distribution is a challenging mission in reservoir simulation. The high degree of heterogeneity and diagenesis in the Nullipore carbonate reservoir provide a significant obstacle to accurate prediction. Moreover, intricate relationships between core and well logging data exist in the reservoir. This study presents a novel approach based on Machine Learning (ML) to overcome such difficulties and build a robust permeability predictive model. The main objective of this study is to develop an ML-based permeability prediction approach to predict permeability logs and populate the predicted logs to obtain the 3D permeability distribution of the reservoir. The methodology involves grouping the reservoir cored intervals into flow units (FUs), each of which has distinct petrophysical characteristics. The probability density function is used to investigate the relationships between the well logs and FUs to select high-weighted input features for reliable model prediction. Five ML algorithms, including Linear Regression (LR), Polynomial Regression (PR), Support Vector Regression (SVR), Decision Trees (DeT), and Random Forests (RF), have been implemented to integrate the core permeability with the influential well logs to predict permeability. The dataset is randomly split into training and testing sets to evaluate the performance of the developed models. The models’ hyperparameters were tuned to improve the model’s prediction performance. To predict permeability logs, two key wells containing the whole reservoir FUs are used to train the most accurate ML model, and other wells to test the performance. Results indicate that the RF model outperforms all other ML models and offers the most accurate results, where the adjusted coefficient of determination (R2adj) between the predicted permeability and core permeability is 0.87 for the training set and 0.82 for the testing set, mean absolute error and mean squared error (MSE) are 0.32 and 0.19, respectively, for both sets. It was observed that the RF model exhibits high prediction performance when it is trained on wells containing the whole reservoir FUs. This approach aids in detecting patterns between the well logs and permeability along the profile of wells and capturing the wide permeability distribution of the reservoir. Ultimately, the predicted permeability logs were populated via the Gaussian Random Function Simulation geostatistical method to build a 3D permeability distribution for the reservoir. The study outcomes will aid users of ML to make informed choices on the appropriate ML algorithms to use in carbonate reservoir characterization for more accurate permeability predictions and better decision-making with limited available data.

Study of the efficiency of machine learning algorithms based on data of various rocks

Background: Absolute permeability plays an important role in studying the fluids flow in porous media during the development of oil and gas reservoirs, the injection of CO2 into reservoirs for storage, the monitoring of pollutants migration in underground aquifers, and the modeling of catalytic systems. Therefore, an accurate and fast evaluation of its values is an important task.
 Aim: The purpose of this article is to study the applicability of machine learning methods for predicting the absolute permeability of carbonate samples, as well as ways to improve the prediction of permeability.
 Materials and methods: The input data is 408 small volumes extracted from four cylindrical carbonate samples composed almost entirely of calcite. Input data includes total and connected porosity, specific surface area, radii of all and only connected pores, coordination number, throat radius and length, tortuosity, and absolute permeability. Permeability prediction is carried out using regression machine learning methods such as random forest, extremely random trees and extended gradient boosting. Parameters (data) of small volumes were determined using pore-scale modeling of water flow in their pore space applying a specialized Avizo software.
 Results: Data of small volumes extracted from fractured and non-fractured samples were analyzed, and the results showed that there are good relationships between many parameters of small volumes. For example, the connected and total porosity have a second-order polynomial relationship with a high correlation coefficient. Using the above-mentioned regression machine learning methods, absolute permeability values were predicted when input data divided into training and testing data in a ratio of 80/20 and 70/30.
 Conclusion: Using the logarithm of permeability instead of permeability itself and considering fractured and non-fractured samples separately, can increase the accuracy of absolute permeability prediction using the above-mentioned machine learning methods up to 90%. The extremely random trees method is the most accurate among the three machine learning methods considered for our task.

A viscosity-soluble type biomimetic self-healing cement slurry system

With the development of cementing slurry technology, cementing self-healing has become one of the most effective technologies to prevent and treat oil and gas channeling and improve long-term sealing capacity of cement sheath. To solve the technical problem of annulus oil and gas channeling caused by the long-term sealing failure of cement sheath, this paper researches and develops a kind of viscosity-soluble type biomimetic self-healing cement slurry system from the perspective of chemical biomimetics, based on the technical idea of “prevention before blocking”. Then, the engineering performance of this biomimetic self-healing cement slurry system and the mechanical property of self-healing set cement are evaluated respectively. In addition, the mechanical recovery capacity and permeability reducing rate of the self-healing set cement conserved with methane are evaluated by creating fractures through splitting. Finally, the healing mechanisms of the self-healing cement are analyzed by means of SEM microscopic test and infrared spectrography. And the following research results are obtained. First, the set cement has excellent mechanical property. When the compressive strength decreases by 15 %, the elastic modulus of set cement decreases by 41 %, showing good toughness to play the role of “first prevention”. Second, when meeting hydrocarbon, the damaged set cement is excited to generate a kind of viscosity-soluble type gelatinous ester substance, which re-bonds the skeleton of the set cement. When the self-healing set cement is conserved with methane, its compressive strength recovery rate is up to 94.5 %, bending strength recovery rate is 58.6 %, gas log permeability drops by 99 %, and liquid log permeability drops by 100 %, so as to realize “blocking later”. In conclusion, the viscosity-soluble type biomimetic self-healing cement slurry system can effectively achieve cementing integrity and long-term sealing integrity through self-healing after the damage of cement sheath, which provides a strong technical support for preventing the production safety hazard of sustained casing pressure in oil and gas wells and is beneficial to solve the technical problem of annulus oil and gas channeling caused by long-term sealing failure of cement sheath.

Depositional and Diagenetic Controls on Reservoir Quality of Neogene Surma Group from Srikail Gas Field, Bengal Basin, Bangladesh

The development of an effective and profitable exploration and production depends heavily on the quality of the reservoir. The primary goal of this study was to evaluate the reservoir quality of the Neogene Surma Group at the Srikail Gas Field, which is situated in the western part of the eastern folded belt of the Bengal Basin, Bangladesh. Wire-line logs, core analysis, petrography, X-Ray diffraction (XRD) and a scanning electron microscope (SEM) were used to understand the depositional and diagenetic controls of the quality of the reservoir. The Surma Group of the Srikail Gas Field was deposited in a delta system with a dominant influence of tide. The subarkosic to sublitharenitic Neogene Surma Group sandstones have primary porosities ranging from 0% to 25.8%, with an average of 21.5%, and the secondary porosity is approximately 7%. The range of log porosity ranges from 15% to 22.2%, while log permeability and core permeability vary from 3.01 to 54.09 mD and 0.1 to 76 mD, respectively. The primary porosity had been destroyed mainly by mechanical and ductile grain compaction. Most of the clay minerals (illite/illite-smectite, chlorite and kaolinite) in sandstone occur as grain coatings, grain lining (rim) and a few occur as pore-filling. This study reveals that the reservoir quality is predominantly controlled by the depositional environment (sediment texture and facies, ductile grain supply, clay content), and diagenetic process (mainly mechanical and ductile grain compaction followed by clay cement). The information gathered from this research will be useful for future petroleum production and for enhancing predictability in order to find new prospects.

Characterization of Porcine Gingiva for Drug Absorption

Gingiva or gum is a part of the periodontium that surrounds the tooth. Its main function is to provide an effective barrier to both mechanical trauma and bacterial invasion. Gingiva is the target site for some topical drugs. The most common disease in gingiva is periodontal diseases (gum infections). Understanding the gingiva barrier properties could provide insights into approaches to effective drug delivery for the gingiva. Porcine gingiva was chosen as the model in the present membrane transport study. The permeability coefficients of gingiva were determined using a modified Franz diffusion cell with small diffusional area (0.03 cm2) and 12 model permeants with different physicochemical properties. The influences of edge effect and aqueous boundary layers were not observed in the modified diffusion cell setup for the small pieces of gingiva tissue samples. Lipophilic permeants exhibit higher permeability coefficients than hydrophilic permeants. A correlation was observed between the Log permeability coefficient (Log P) and Log octanol-water distribution coefficient (Log Dow) in the analysis. The permeant molecular weight (MW) was also a factor in the Log P vs. Log Dow relationship. The coefficient of Log Dow in this three-factor relationship (0.42) suggested that the gingiva barrier was less lipophilic than octanol.

Permeability prediction in the South Georgia rift basin–applications to CO2 storage and regional tectonics

Absence of a permeability log necessary to assess reservoir quality and injectivity for potential CO2 storage in the heterogeneous and complex South Georgia Rift (SGR) basin provides the motivation for this study. The focus of this study was on the Triassic-Jurassic red beds buried, entrenched beneath the Cretaceous-Cenozoic Coastal Plain sediments. Moreover, the significant cost typically between $10 M and $100 M associated with drilling and logging for in situ permeability coupled with the limited resolution of existing core data further makes this work necessary. The purpose is to relate, use the interpretation of the predicted permeability distribution to assess feasibility for safe and long-term CO2 sequestration. This study also intends to establish the impacts of active and passive tectonism that has shaped and/or re-shaped the evolution of the basin on the present-day permeability. A methodology was applied that utilizes the pore space and geohydraulic properties of the reservoir from existing laboratory and well data to produce a newly derived permeability log. It shows a non-uniform distribution with depths possibly due to geologic changes in the confined and heterogeneous red beds. The derived log displays characteristics consistent with observations from the porosity and resistivity logs. The interpretation of these logs provides evidence for the presence of low permeable, tightly cemented, and compacted red beds. We conclude that the low permeability aided by the low resistivity depicted in the red beds suggests increased confining stress and reduced injectivity, and that the uncharacteristically low permeability reflects a deformed basin shaped with episodes of uplift and erosion.

Determining the Difference in Homogeneity between the Euphrates and Jeribi Formations in the Qayyarah Oil Field Based on Vertical and Horizontal Core Samples

Permeability and Porosity are the important petrophysical parameters of the hydrocarbon reservoirs for petroleum production. In the Qayyara oilfield, vertical and horizontal core samples were collected from the Jeribe and Euphrates formations to perform various tests. An exponential relationship between porosity and logarithm of permeability for 123 vertical samples was derived and explained based on the compaction and shale size. The difference in homogeneity and shale size between the two formations was demonstrated by the coefficient of determination. A Log-Log plot between vertical and horizontal permeability was derived using vertical and horizontal core samples. An equation for predicting vertical permeability from horizontal one was produced. In the same way, a linear relationship between vertical and horizontal porosity was deduced and interpreted based on homogeneity. The ratio between the vertical and horizontal permeability was set to determine the degree of anisotropy at various depths.

A novel approach by integrating the core derived FZI and well logging data into artificial neural network model for improved permeability prediction in a heterogeneous gas reservoir

Predicting and extrapolating the permeability between wells to obtain the 3D distribution for the geological model, is a crucial and challenging task in reservoir simulation. Permeability is influenced by both digenetic characteristics and depositional factors like sorting and grain size. Hence, a reliable model should consider these characteristics for prediction of permeability. Grouping the rocks into different hydraulic flow units (HFU) or discrete rock types (DRT), improves the identity of the reservoir characteristics and provide a more accurate permeability prediction. Multi variable regression models and Artificial Neural Networks (ANN) were applied in this study to correlate core permeability and porosity with well logs to predict permeability logs. It was observed that the accuracy of the models diminished in heterogeneous reservoirs, where there is a wide permeability distribution.In this study, we are presenting a novel approach to predict permeability in heterogeneous oil and gas reservoirs. In this method the core permeability and porosity data are categorized using the concept of DRT and the probability density functions are used to investigate the relationships between the logs and DRT groups. The ANN model is applied to correlate the core derived flow zone indicator (FZI) with wire-line logging data with a single key well to predict K-logs. In this approach one single well, which contains all DRT groups is considered as a key well to develop and train the ANN model. It was observed that ANN model exhibits better prediction performance in heterogeneous reservoirs when it is developed and trained on single well data containing all DRT groups. This approach can capture heterogeneity in the reservoirs where it has been applied successfully to predict permeability in an actual heterogeneous carbonate gas reservoir.

Deep learning for predicting permeability logs in offset wells using an artificial neural network at a Waggoner Ranch reservoir, Texas

Permeability logs are critical data used to assess reservoir quality, which can impede the confidence of reservoir productivity without proper characterization. In particular, this can be significant in shale-sand sequence reservoirs that have heterogeneous lithology. Fortunately, permeability logs can be derived from Stoneley waves that are extracted from conventional full-waveform sonic logs. Because sonic logs are not always available in old wells, exploring alternatives to predict permeability is essential. The artificial neural network method is one option that provides elements to develop a model to predict permeability logs in offset wells. Consequently, a model is constructed using standard logs as input data and a Stoneley-wave permeability log as the desired output. These logs are obtained from a new well containing a complete suite of logs including full-waveform sonic data and a permeability log. The open-source Python library Keras provides the environment to generate the model. The model is applied to two nearby wells in which permeability logs do not exist. The borehole separation from the new well to the far well is 2055 ft, and the separation distance to the near well is 1370 ft. The logging data were acquired from wells located in an oil reservoir at Waggoner Ranch in northeast Texas. The reservoir geology is a sand-shale sequence intercepted by thin limestone markers at several depths. Permeability logs are predicted and correlated with other logs and their corresponding lithology. Sands partially saturated with hydrocarbons are identified by their permeability properties at the wells. The sands were previously characterized and mapped from impedance data using a nonlinear inversion of reflection seismic image constrained with well logs. The data analysis demonstrates the usefulness of the neural network approach to predict permeability in reservoirs where offset wells are available and only one well contains a permeability log.

A lattice Boltzmann simulation on the gas flow in fractal organic matter of shale gas reservoirs

The gas flow in the organic matter with multiple flow regimes is critical to the shale gas production from shale gas reservoirs, but simultaneously simulating the full flow regimes of shale gas in the nanopores of the fractal shale organic matter is still unavailable. This paper presents a modified lattice Boltzmann model with effective relaxation time to simulate the shale gas micro-flow behaviors in fractal organic matter for permeability prediction. Firstly, some fractal shale organic matters are reconstructed by the quartet structure generation set (QSGS) algorithm, and the structural parameters such as fractal dimension, lacunarity and average pore diameter are calculated to characterize the complexity of the fractal shale organic matter. Secondly, effective viscosity and Knudsen layer are introduced into the effective relaxation time to formulate a modified lattice Boltzmann model. Thirdly, this modified lattice Boltzmann model is verified by comparing with the theoretical permeability models for the gas flow in the body centered cubic (BCC) arrays. Finally, a series of gas flow simulations in reconstructed fractal shale organic matter are conducted to explore the effects of structure parameters on permeability. Numerical simulations show that the modified lattice Boltzmann model can simulate the full gas flow regimes and predict the permeability of the fractal shale organic matter. The logarithm of permeability is negatively linearly correlated with fractal dimension and positively linearly correlated with lacunarity of the shale organic matter. This permeability exponentially increases with the increase of average pore diameter and a power law is observed between the fractal dimension and average pore diameter of these fractal shale organic matters. The anisotropic structures have great impacts on the directional permeability of shale organic matter. The permeability kx in the horizontal direction increases first and then decreases with the increase of anisotropic ratio (AR) and reaches the peak when AR is about 20. The permeability ky in the vertical direction monotonically decreases with the increase of AR.

Technology Focus: Well Testing (February 2022)

Bias is always a factor in decision making. To put it a catchier way, “if you have a brain, you are biased.” This was the opening to a recent soft-skills training course I took on the science of better decision making. I left pondering the implications of bias on pressure transient analysis. Well tests are conducted throughout the resource life cycle to establish well and reservoir properties and to optimize depletion planning. But this requires well-test interpretation, which, in turn, requires choosing among nonunique solutions. One key to reducing nonuniqueness is to integrate pressure data with information from other disciplines, including - Routine core analysis, core description, and well logs - Formation-tester mobilities and interval pressure transient tests - Production logging and distributed temperature surveys - Fieldwide production and injection history - Seismic data and geologic understanding This is where technology comes in. New tools have made the unmeasureable measurable and have provided data in larger quantities with improved precision and for longer periods of time. While it may be an engineer’s dream, more and better data do not necessarily lead to better decisions. As with any sound scientific or engineering study, one must think about bias. Is the data being unconsciously cherry-picked to mold the desired outcome? After all, the subsurface is complex enough that multiple assumptions and hypotheses remain plausible; not all information may fit the storyline, and some might be dismissed or underweighted before the full range of possibilities is considered. A critical component of interpretation includes acknowledging one’s own bias and carefully listening to and seriously considering alternative and opposing views. Cold-eyes reviews and devil’s advocates are part of a centuries-old toolkit proven to root out bias, ambiguity, and unnecessary complexity, leading to better-defined problems and, ultimately, better decision making. Interpreting data from new and well-established technologies under the framework of the Socratic method paves the way to a clearer view of the subsurface. This month’s papers demonstrate the continued relevance and diversity of our discipline. The ambitious permeability log, referred to as “elusive” in my column 2 years ago, now boasts a tangible pilot in a Middle Eastern carbonate field. Well tests and cleanup operations do not always require flaring. Alternative fluid-disposal solutions, tailored to project specifics, show potential to meet the timely topic of emissions. Machine learning offers a key to deciphering well connectivity in field data, overcoming noise and other suboptimal conditions. Recommended additional reading at OnePetro: www.onepetro.org. IPTC 21780 - Design and Implementation of a Water-Based Micronized Weighting Agent Fluid System for Deepwater Drillstem Testing Operation in Environmentally Restricted Location by Marcelo Dourado Motta, MI-SWACO, a Schlumberger Company, et al. SPE 205145 - Systematic Application of Pressure and Temperature Transient Analysis in an Oil Field: A Case Study by Khafiz Muradov, Heriot-Watt University, et al.

Interval inversion based well log analysis assisted by petrophysical laboratory measurements for evaluating tight gas formations in Derecske through, Pannonian basin, east Hungary

The evaluation of Hungarian tight gas formations requires the use of innovative interpretation technologies because of diverse geological environment, heterogeneous dataset including several variables measured with different accuracies, and reservoirs under varying petrophysical conditions. Conventional well logging methods based on the use of limited number of wireline logs make only a quick-look interpretation, in which a limited number of unknowns is determined, and there is a non-negligible risk for occurrence of ambiguity. In order to increase the overdetermination (data-to-unknowns) ratio and the estimation accuracy and reliability, all suitable well logs are to be jointly processed in a more effective manner. To estimate the total organic carbon content together with porosity, water saturation, shale content and rock matrix volumes with higher accuracy, a new inversion methodology is introduced that is tested in tight gas reservoirs. A differential evolution algorithm-based interval inversion method is developed that is combined by nano-permeability and high precision porosity laboratory measurements for the detection and evaluation of Hungarian tight gas reservoirs. Spectral gamma-ray, neutron-porosity and electric resistivity logs are jointly analyzed using the interval inversion approach. By solving a highly overdetermined inverse problem, the effective porosity, water- and gas saturation, shale and sand content, as well as the kerogen volume are estimated in a reliable inversion procedure. The silt and clay content of Miocene tight gas formations are separated and both determined by inversion, while the total organic carbon content is derived from the inversion results. The permeability log is directly derived from the inverted porosity values using the local porosity versus permeability relation established by multi-well core analysis. The combination of in situ wireline log data analysis and porosity and nano-permeability laboratory measurements is shown to be advantageous for a more reliable interpretation of unconventional hydrocarbon formations. An added advantage of the interval inversion method is the calculation of depth dependent errors of the estimated petrophysical quantities. It is shown that total organic carbon and other volumetric parameters can be determined with high accuracy in one interpretation procedure. In the Derecske basin, East Hungary, gas indications are confirmed by interval inversion, that were previously made it probable according to measurements made on poor core samples and rather noise-sensitive local well logging inversion procedure. The joint evaluation of new core data and original wireline logs using the proposed methodology may reveal further potential tight gas resources in the studied area. The Hungarian case study confirms that the suggested workflow may serve a new alternative for an improved evaluation of tight gas formations in different other hydrocarbon fields.

Empirical and theoretical analysis of a modified isochronal test in a caspian region gas reservoir

A deliverability test performed in a gas well is one of the fundamental steps to determine the flow potential of the wells in reservoir surveillance. Dynamic reservoir models need robust AOF (Absolute Open Flow), productivity index, permeability for calibration, history matching, and predictions. In this study, a gas well in a Pliocene sandstone gas reservoir was validated using the “Modified Isochronal Testing” method. This study aims to mitigate the efforts done to estimate the phenomenon of flow potential of the gas wells. Hence, the field development activities can be leveraged to a more efficient stage technically and economically. In this study, AOF, Productivity Index (PI), and the permeability of the tested interval were calculated. The validation of the permeability with log and core data increases the confidence level of the work done. AOF is the key performance indicator to define the gas well productivity at zero sand face pressure which is a measure of ranking the production potential of gas wells. The AOF values support the determination of the number of wells to be drilled and the design of the surface facilities. In this study, pressure and time data were obtained and they were analyzed by empirical and theoretical methods. These two methods used in analysis gave very close AOF values; 10.15MMSCF/D (empirical) and10.67 MMSCF/D (theoretical), respectively. The permeability was calculated as 3.42 md, which is in line with the log permeability of 3.70 md and core permeability of 3.40 md.

Fracture permeability estimation utilizing conventional well logs and flow zone indicator

Characteristics of the natural open fractures on the oil and gas reservoirs is crucial in drilling and production planning. Direct methods of fractures studies such as core analysis and image log interpretation are usually not performed in all drilled wells in a field. Therefore, in absence of these data, the indirect methods can play an important role. In this study, an integrated algorithm is introduced to identify the fractures and estimate its permeability employing conventional well logs. First, open fractures were identified and their properties including density, aperture, porosity and permeability were estimated using FMI log. Subsequently, the fracture index log (FR_Index) was estimated utilizing conventional logs including density, micro-resistivity, sonic (compressional, shear and stoneley slownesses), and caliper logs. After that, the fracture index permeability was estimated by improving the FZI permeability equation. The coherence coefficient between two estimated fracture permeability logs is 0.66. A good correlation is observed on the high permeability zones, but the lower correlation on the low permeability zones. It is notified that, in the high fracture permeability zones, the conventional logs are heavily impacted by fracture permeability. However, due to lower vertical resolution of conventional logs compared with the image logs, the conventional logs are less influenced by less dense fracture zones. However, this algorithm can be used with acceptable accuracy in all uncored and image log wells.

Estimation of nuclear magnetic resonance (NMR) log permeability by integrating artificial neural network and imperialist competition optimization algorithm (ICA) in one of the oil fields of southwestern Iran

Estimation of nuclear magnetic resonance (NMR) log permeability by integrating artificial neural network and imperialist competition optimization algorithm (ICA) in one of the oil fields of southwestern Iran

Penetration and Displacement Behavior of N2 in Porous Interlayer Structures Containing Water/Salt Component by Molecular Dynamics Simulation.

The penetration and displacement behavior of N2 molecules in porous interlayer structures containing a water/salt component with porosities of 4.29%, 4.73%, 5.17%, 7.22%, and 11.38% were explored using molecular dynamics simulations. The results demonstrated that the large porosity of the interlayer structures effectively enhanced the permeation and diffusion characteristics of N2. The water and salt in the interlayer structures were displaced during the injection of N2 in the porosity sequence of 4.29% < 4.73% < 5.17% < 7.22% < 11.38%. The high permeance of 7.12 × 10−6 indicated that the interlayer structures with a porosity of 11.38% have better movability. The strong interaction of approximately 15 kcal/mol between N2 and H2O had a positive effect on the diffusion of N2 and the displacement of H2O before it reached a stable equilibrium state. The distribution of N2 in porous interlayer structures and the relationship between the logarithm of permeability and breakthrough pressure were presented. This work highlighted the effects of porosity on the permeability and diffusion of N2/H2O in the interlayer, thus providing theoretical guidance for the development of petroleum resources.

Evaluating numerical simulation errors of CO[formula omitted]-brine flow with capillary heterogeneity using a 1D semi-analytical solution

Modeling CO2-brine flow related to CO2 storage in saline aquifers involves intense numerical simulations. One of the important processes associated with these flows, allowing for safe long term storage of CO2, is residual trapping. For accurate modeling of trapping, capillary heterogeneity should be incorporated in the models. However, there have been some reports of increased numerical simulation errors associated with these models and yet no systematic study of their accuracy has been carried out.We compare results of two numerical simulators to a semi-analytical solution of 1D two-phase immiscible steady state flow with capillary heterogeneity. Simulation errors are calculated for a wide range of different flow rates, fluid fractions, capillary pressure curves and heterogeneities. Results show that capillary pressure and saturation errors increase with smaller dimensionless capillary number and reach large values of 25−50% for many cases. It is shown that the errors are related to capillary heterogeneity and therefore increase also with larger variance of log permeability. Finally, a new approach for predicting numerical errors without employing an analytical solution is proposed, i.e., the error indicator analysis.

Pharmacokinetic predictions and docking studies of substituted aryl amine-based triazolopyrimidine designed inhibitors of Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH)

BackgroundThe sixteen (16) designed data set of substituted aryl amine-based triazolopyrimidine were docked against Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) employing Molegro Virtual Docker (MVD) software and their pharmacokinetic property determined through SwissADME predictor.ResultsThe docking studies shows compound D16, 5-((6-methoxy-5-methyl-[1,2,4]triazolo[1,5-a]pyrimidin-7-yl)amino)benzo[b]thiophen-4-ol to be the most interactive and stable derivative (re-rank score = − 114.205 kcal/mol) resulting from the hydrophobic as well as hydrogen interactions. The hydrogen interaction produced one hydrogen bond with the active residues LEU359 (H∙∙H∙∙O) at a bond distances of 2.2874 Å. All the designed derivatives were found to pass the Lipinski rule of five tests, supporting the drug-likeliness of the designed compounds.ConclusionThe ADME analysis revealed a perfect concurrence with the Lipinski Ro5, where the derivatives were found to possess good pharmacokinetic properties such as molar refractivity (MR), number of rotatable bonds (nRotb), log of skin permeability (log Kp), blood-brain barrier (BBB). These results could a deciding factor for the optimization of novel antimalarial compounds.