Permeability Carbonate Reservoirs Research Articles

Fluid inclusion records of oil-cracked wet gas in Permian carbonate reservoirs from the Eastern Sichuan Basin, China

Oil-cracked gas is an important gas resource in petroliferous basins. In theory, oil-cracked gas should be rich in C2+ (i.e., wet gas). However, gas reservoirs of oil cracking origin in the Sichuan Basin (South China) are mainly composed of dry gas. Fluid inclusions record hydrocarbon generation and evolution. Obviously, identifying wet gas inclusions is the key to solving the above controversy. Here, we calibrate the Raman spectroscopic detection limits of ethane and propane (0.007 and 0.016 MPa, respectively) and demonstrate that Raman spectroscopy is a powerful method to identify C2+-bearing inclusions. Based on this technique, C1–C2–C3-bearing wet gas inclusions (C1/ƩCi = 0.90–0.93) are identified in calcite cements of the Permian Changxing Formation carbonate reservoir in the Eastern Sichuan Basin. These inclusions are direct geological evidence of oil-cracked gas because the calcite cement precipitated after the formation of pyrobitumen. Furthermore, reconstruction of the temperature-pressure-composition evolutions of calcite-hosted (Changxing Formation) and quartz-hosted gas inclusions in the Ordovician Wufeng–Silurian Longmaxi Formation black shale indicates that high pressure can effectively retard the cracking of C2+ gas hydrocarbons. Differences in gas composition and δ13CCH4 signature between reservoir gas and inclusion gas lead us to conclude that large-scale oil cracking occurred during the Early–Middle Jurassic in the Permian Changxing carbonate reservoir. Nevertheless, oil-cracked gas escaped during the evolution of the paleo-gas reservoir. Formation of the current dry gas reservoir can be attributed to the continuous mixing of late-stage kerogen pyrolysis gas.

Episodic hydrothermal alteration on Middle Permian carbonate reservoirs and its geological significance in southwestern Sichuan Basin, SW China

To analyze the episodic alteration of Middle Permian carbonate reservoirs by complex hydrothermal fluid in southwestern Sichuan Basin, petrology, geochemistry, fluid inclusion and U-Pb dating researches are conducted. The fractures and vugs of Middle Permian Qixia–Maokou formations are filled with multi-stage medium−coarse saddle dolomites and associated hydrothermal minerals, which indicates that the early limestone/dolomite episodic alteration was caused by the large-scale, high-temperature, deep magnesium-rich brine along flowing channels such as basement faults or associated fractures under the tectonic compression and napping during the Indosinian. The time of magnesium-rich hydrothermal activity was from the Middle Triassic to the Late Triassic. The siliceous and calcite fillings were triggered by hydrothermal alteration in the Middle and Late Yanshanian Movement and Himalayan Movement. Hydrothermal dolomitization is controlled by fault, hydrothermal property, flowing channel and surrounding rock lithology, which occur as equilibrium effect of porosity and permeability. The thick massive grainstone/dolomites were mainly altered by modification such as hydrothermal dolomitization/recrystallization, brecciation and fracture-vugs filling. Early thin–medium packstones were mainly altered by dissolution and infilling of fracturing, bedding dolomitization, dissolution and associated mineral fillings. The dissolved vugs and fractures are the main reservoir space under hydrothermal conditions, and the connection of dissolved vugs and network fractures is favorable for forming high-quality dolomite reservoir. Hydrothermal dolomite reservoirs are developed within a range of 1 km near faults, with a thickness of 30–60 m. Hydrothermal dolomite reservoirs with local connected pore/vugs and fractures have exploration potential.

Estimation of pore structure and permeability in tight carbonate reservoir based on machine learning (ML) algorithm using SEM images of Jaisalmer sub-basin, India

Analyzing the pore structure in carbonate reservoirs plays a crucial role in predicting fluid flow characteristics within these formations. The goal of the study was to use machine learning techniques for pore structure analysis and estimation of permeability in carbonate reservoirs. We implemented these algorithms by examining 2D scanning electron microscope (SEM) images of carbonate samples from the Jaisalmer sub-basin captured at various magnifications. In the initial stage of the analysis, various binarization algorithms were applied to determine carbonate sample porosity. Among these algorithms, the MaxEntropy algorithm gave a porosity value closely aligned with those obtained through petrography analysis. We employed the watershed algorithm to find the pore network parameters of carbonate samples at various magnifications. We observed that changes in magnification affected pore network parameters, resulting in a reduction in pore size distribution, throat radius, and grain size. Subsequently, we employed the numerical lattice Boltzmann method (LBM) to estimate the permeability of carbonate samples and compared to values derived from well logs. We employed machine learning (ML) algorithms, specifically Artificial Neural Network (ANN) and Support Vector Machine (SVM), to predict the permeability of carbonate samples. The input features for these models were the pore network parameters, while the LBM permeability values served as the output. We examine the prediction performance of these methods against the measured LBM permeability by conducting the error analysis and the coefficient of determination (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}^{2}$$\\end{document}) calculation. Our findings revealed that the ANN models outperformed the SVM models. Specifically, the ANN model displayed an impressive R2 value of 0.892, along with root mean square error (RMSE), mean squared error (MSE) and, mean absolute error (MAE) values of 1.927, 3.716 and 1.580, respectively. In contrast, the SVM model yielded an R2 value of 0.849, with RMSE, MSE and, MAE values of 2.324, 5.401 and, 2.166 respectively, when assessed on testing data of measured permeability. This study found that ANN is more dependable, robust, and precise than SVM in forecasting carbonate sample permeability.

A New Approach to Predicting Vertical Permeability for Carbonate Rocks in the Southern Mesopotamian Basin

Reservoir performance depends on many factors, and the most important one is permeability anisotropy. In addition, with high heterogeneity, it is essential to find unique relationships to predict permeability. Therefore, this study aims to predict vertical permeability based on horizontal permeability and porosity and to find new equations for carbonate reservoirs. This work relied on the 398 measured points of cores data collected from several wells in carbonate reservoirs. A new correlation for predicting vertical permeability for the whole data (369 samples) as a function of horizontal permeability and porosity has been developed. The results indicate that this new correlation can estimate the vertical permeability with correlation coefficients (RSQ) of 0.853. Then, the used data were divided into four groups depending on the Kv/Kh values: less than 0.1, 1–0.1, 1–10, and more than 10, and a new correlation for permeability prediction for each group has been developed with good RSQ values of 0.751, 0.947, 0.963, and 0.826, respectively. The previous studies lack the correlations to predict vertical permeability in carbonate reservoirs, so this study can be considered as a reference for similar cases.

Fracture identification and its application of ultra-low permeability carbonate reservoir in Fauqi North oilfield, Iraq

Fracture identification and its application of ultra-low permeability carbonate reservoir in Fauqi North oilfield, Iraq

Ensemble Learning Based Sustainable Approach to Carbonate Reservoirs Permeability Prediction

Permeability is a crucial property that can be used to indicate whether a material can hold fluids or not. Predicting the permeability of carbonate reservoirs is always a challenging and expensive task while using traditional techniques. Traditional methods often demand a significant amount of time, resources, and manpower, which are sometimes beyond the limitations of under developing countries. However, predicting permeability with precision is crucial to characterize hydrocarbon deposits and explore oil and gas successfully. To contribute to this regard, the current study offers some permeability prediction models centered around ensemble machine learning techniques, e.g., the gradient boost (GB), random forest (RF), and a few others. In this regard, the prediction accuracy of these schemes has significantly been enhanced using feature selection and ensemble techniques. Importantly, the authors utilized actual industrial datasets in this study while evaluating the proposed models. These datasets were gathered from five different oil wells (OWL) in the Middle Eastern region when a petroleum exploration campaign was conducted. After carrying out exhaustive simulations on these datasets using ensemble learning schemes, with proper tuning of the hyperparameters, the resultant models achieved very promising results. Among the numerous tested models, the GB- and RF-based algorithms offered relatively better performance in terms of root means square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) while predicting permeability of the carbonate reservoirs. The study can potentially be helpful for the oil and gas industry in terms of permeability prediction in carbonate reservoirs.

Permeability anisotropy impact on wormhole propagation in openhole and limited-entry completions: A 3D numerical study

The permeability in carbonate reservoirs is not the same in all directions (i.e., anisotropic), a property seldom considered in acid stimulation. The study hypothesizes that permeability anisotropy impacts acid treatment efficiency significantly. The investigation included the openhole and limited-entry completions in a vertical and horizontal wellbores configuration. A model based on the two-scale continuum approach was constructed in three-dimensional space (3D), resembling the big block experiment with the wellbore at the center. Permeability anisotropy significantly impacts acid efficiency in terms of the pore volume to breakthrough (PVBT) and optimum injection rate. For instance, simulations of acid placement in an openhole horizontal well with 10 anisotropy ratio resulted in PVBT of 0.15 compared to 0.35 in a vertical well. Limited entry completions result in more extended wormholes where the permeability anisotropy positively impacted wormhole length as compared to isotropic permeability. Nevertheless, the permeability anisotropy negatively impacted wormhole coverage along the wellbore in most scenarios. The simulations showed that a spear-like wormhole propagates from a limited entry horizontal wellbore in anisotropic formation, which previous studies assumed to be spherical or ellipsoidal. This implies that the skin factors models should be revised based on the new findings. This is the first study to assess the permeability antitropy impact on the stimulation of openhole and perforated wells in a 3D space.

Experimental evaluation and optimization of improved conductivity of gelling acid etched fractures in deep, low-permeability reservoirs

Stimulation technology is the key to efficient development of deep low-permeability carbonate reservoirs. For such reservoirs, where the porosity and permeability are low, or where the reservoir is far from the borehole. An efficient production increase can only be achieved by using a deep percolation gelling acid fracture technique, maximizing the length of the acid etched fracture to expand the percolation area, and improving the conductivity of the acid etched fracture. A quantitative experimental study of the conductivity of acid etch fracture based on an acid rock tiling instrument, a 3D laser scanner, and an acid etch fracture conductivity evaluation device identified three typical supporting morphologies of gelling acid etch fracture walls: spot, line, and sheet. A systematic quantitative description and evaluation of the etch morphology of rocks etched by acid has been performed. The fracture morphology of acid etchings has been quantitatively evaluated under various experimental conditions such as fracture width, temperature, acid fluid consumption, displacement, and different acid fracture processes and their laws affecting fracture conductivity. The conductivity of acid corrosion fracture increases with the decrease of fracture width, and decreases with the increase of closure stress. The greater the amount of acid used, the higher the fracture conductivity; With the increase of flow Reynolds number, the conductivity of acid corrosion fracture shows a trend of increasing first and then decreasing. The fracture conductivity was compared and analyzed for different property placement conditions under the acid sand carrying technique, and the main effect of the acid etch fracture conductivity was identified. Optimization of high conductivity and deep penetration gelling acid fracturing techniques can help guide the design of acid fracturing and improve stimulation and recovery effects, thus achieving the goal of efficient development of deep, low permeability carbonate reservoirs to increase reserves and production.

Transient Open‐Closed Loop Experimental Validation of a Nonlinear Two‐Phase Flow Distributed System

AbstractThe oil well drilling process is a nonlinear system with transient nature. Conventional drilling is unable to assure safe and cost‐effective operation for fractured, cavernous, and highly permeable carbonate reservoirs, which contain the largest oil reserves worldwide. Concerning drilling technologies, Pressurized Mud Cap Drilling (PMCD) is suitable for the challenging scenario previously mentioned. According to PMCD technique, a sacrificial fluid is injected through the drill string and a light annular mud is pumped in countercurrent through the annulus region (bullheading), without surface return, forcing gas and drilled cuttings back to formation. A two‐phase flow distributed model (Drift Flux Model – DFM) is developed to properly describe the complex nature of the system. Also, an experimental facility, presenting field similarity, is employed to validate the open – closed loop schemes. The main objective of the controller (control reconfiguration with gain scheduling) is to regulate annulus pressure, handling gas kick, drilling fluid losses and inverse response dynamics. Besides, gas injection, migration and bullheading are studied. The simulations, validated through experimental data, highlight the methodology usefulness for field applications.

Effect of different salinity on low permeability carbonate reservoir recovery using a new green polymeric nanocomposites

ABSTRACT Nanoparticles usage and altering mechanisms such as wettability alteration (CA) and interfacial tension (IFT) reduction in carbonate reservoirs are common methods used to recover more oil (nanoparticles-assisted enhanced oil recovery (EOR)) from these reservoirs. ZnO/SiO2/Xanthan as new nanocomposites were introduced at reservoir conditions and different salinity, and results were compared with commercial SiO2 nanoparticles in this study. Scanning electron microscopy (SEM), X-ray diffraction (XRD), energy-dispersive X-ray (EDX), and Brunauer-Emmett-Teller (BET) tests were used for observing morphology and the crystal structure of reservoir. Then, nanocomposites and SiO2 nanocomposites at 0.05 wt. % were used for seeing IFT and CA parameters at reservoir conditions and different salinity. It was found that nanocomposites have better results during IFT and CA tests compared to SiO2 nanoparticles, and nanocomposites were selected for surveying the salinity effect in the presence of nanocomposites on low permeability carbonate porous media with imbibition tests. CA (2 SW, 10 SW) decreased from (86 ̊, 80 ̊) to (79 ̊, 78 ̊) and (75 ̊, 74 ̊) in the presence of nanocomposites and SiO2, respectively, at 60°C and 1800 Psi. Two other important factors that were changed effectively in the presence of nanocomposites were IFT and stability tests. Nanocomposites show stable behavior at different salinity (ZP at 10 SW: 31.37, ZP at 2 SW: −28.00), and IFT decreased in the presence of 2 SW and 10 SW (higher in 2 SW). Finally, recovery factor was increased at both 2 SW and 10 SW in the presence of nanocomposites and reservoir conditions (2SW: 25.1%, 10 SW: 34.1%).

Modeling Permeability in Different Carbonate Rock Types

In carbonate reservoirs, permeability prediction is often difficult due to the influence of various geological variables that control fluid flow. Many attempts have been made to estimate permeability from porosity by using theoretical and empirical equations. The suggested permeability models have been questionable in carbonates due to inherent heterogeneity and complex pore systems. The main objective of this paper is to provide a workflow to improve the use of existing models (e.g., Kozeny, Lucia, and Winland) to predict permeability in carbonate reservoirs. More than 1,000 core plugs were studied from seven different carbonate reservoirs across the Middle East: mainly Cretaceous reservoirs. The plugs were carefully selected to represent a wide range of properties within the cored intervals. The data set available included laboratory-measured helium porosity, gas permeability, thin-section photomicrographs, and high-pressure mercury injection. Rock textures were analyzed in the thin-section photomicrographs and were classified based on their content as grainy, muddy, and mixed. Special attention was given to the diagenesis effects, mainly compaction, cementation, and dissolution. The texture information was plotted in the porosity-permeability domain and was found to produce three distinct porosity-permeability relationships. Each texture gave a unique porosity-permeability trend, where the extent of the trend was controlled by diagenesis. Rock types were defined on each trend by detailed texture analysis and capillary pressure. Three different permeability equations (Kozeny, Winland, and Lucia) were evaluated to study their effectiveness in complex carbonate reservoirs. Both Kozeny and Lucia models honored the geology of the samples and showed similar trends to the porosity-permeability relationships, whereas the Winland model gave different slopes to the experimental data. The prediction of the permeability was improved by using different model parameters per RRT within each texture. This work presents a systematic approach to construct correlations between porosity and permeability in complex carbonate reservoirs. Model parameters (i.e., FZI, RFN, and r35) were suggested within different geological rock types to estimate permeability. Based on the workflow presented in the paper, the predicted permeability was improved to less than a factor of 2 compared to the measured values. Moreover, the same workflow was applied using the data from seven different reservoirs, and the same rock typing scheme was applicable to all the reservoirs. Such work is not abundant in the literature and would serve to improve permeability prediction in heterogeneous carbonate reservoirs, which is one of the main uncertainties in modeling carbonates.

Formation damage reduction during CO2 flooding in low permeability carbonate reservoir with using a new synthesized nanocomposites

Oil reservoir production rates gradually decline as reservoir pressure lowers. There are many causes for this, including precipitation and asphaltene (ASP) deposition. This investigation seeks to assess the performance of Synthetic Zeolite-Zirconia-Cerium oxide (ZZC) nanocomposites (NCs) in the prevention of ASP damage using a coreflooding test under reservoir circumstances. In this research, used SEM, XRD, and EDX analyses to prevent ASP precipitation in the static phase and address ASP precipitation issues in the dynamic CO2 (CF) flooding test. Using Na-ZSM-5 zeolite nanoparticles (NPs), CO2-oil IFT experiments, isotherm models, and CO2 core flooding (CCF) tests at the static stage were carried out in the presence of ZZC. After that, CCF tests at the dynamic stage were created under the static conditions that had been established in order to examine permeability/porosity reduction in porous media. In comparison to Na-ZSM-5 zeolite NPs, ZZC absorbed more ASP. Additionally, ZZC and Na-ZSM-5 zeolite had decreased ASP precipitation when the pressure dropped from 2600 to 1700 Psi. The ASP depositions in the dynamic phase were reduced after adding ZZC, and the permeability/porosity reduction characteristics were enhanced. The results of this study may be used to address ASP precipitation during production phases.

Analysis of factors influencing recovery of low permeability and strong heterogeneous gas reservoirs and establishment of prediction model

Jialingjiang Formation is a typical low permeability carbonate reservoir in Sichuan Basin, which is characterized by strong heterogeneity, low matrix permeability and high-water saturation. Based on seven influencing factors, such as permeability, reservoir thickness and reservoir porosity, this paper evaluates and predicts the oil recovery of Jialingjiang Formation gas reservoir by using two neural network models of multilayer perceptor and radial basis function and nonlinear surface fitting method. The results show that: 1) In the neural network prediction model, the correlation coefficient between the prediction result curve of multilayer perceptron and the original recovery curve reaches 0.88, which is higher than the radial basis function (0.81), indicating that the use of multilayer perceptron can better predict the recovery of gas reservoirs. 2) In the nonlinear curved surface fitting prediction model, the two influencing factors with the greatest linear correlation with the recovery factor, reservoir thickness and gas recovery rate, are selected to fit the prediction model, and the prediction model is obtained. According to the prediction model, the recovery factor is positively correlated with the reservoir thickness and gas recovery rate on the whole. The model can be used to estimate gas recovery from two factors: reservoir thickness and gas recovery rate.

Diagenesisand Porosity Evolution Of The Deeply Buried Permian Carbonate Reservoirs: Insight from Maokou Formation in the Sichuan Basin, Southwestern China

Diagenesisand Porosity Evolution Of The Deeply Buried Permian Carbonate Reservoirs: Insight from Maokou Formation in the Sichuan Basin, Southwestern China

Permeability Estimation From stoneley Waves in Carbonate Reservoirs

Permeability is one of the petrophysical properties of oil and gas reservoirs and is defined as the ability of rock to transmit fluids through the porous media. After exploration of any reservoir, permeability information is necessary to optimize the well completion method, oil and gas production and field development. Permeability is determined by both direct and indirect methods. Direct methods are core analysis, well testing, and modular dynamic tester (MDT) and the indirect method is using well logging data such as nuclear magnetic resonance (NMR) and porosity. Determination of permeability from the Stoneley slowness is one of the indirect and continuous methods in the whole well-bore and has been chosen as the goal of this study. The result of this correlation has been plotted against other well logging data and there is a very good match between this result and other petrophysical properties. Due to the complex nature of permeability in carbonate reservoirs, most of the time there is not a good match between this parameter and other petrophysical properties. This study has been conducted on the data of a single well and correlation has been determined. The results show that in calculation of permeability from Stoneley waves, the effective parameters are porosity, lithology, Stoneley slowness and accuracy of the MDT tool. For more precise correlation in a reservoir or a specific geological area, more data from other wells or reservoirs are necessary.

Extensive Well-Testing Operations Provide Insight Into Khuff Reservoirs

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 203441, “Lessons Learned From Extensive Well-Testing Operations in Khuff Formations Offshore Abu Dhabi,” by Florian Hollaender, SPE, Schlumberger, and Mahmoud Basioni and Ahmed Yahya Al Blooshi, ADNOC, et al., prepared for the 2020 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, held virtually from 9-12 November. The paper has not been peer reviewed. An extensive appraisal campaign was performed in the Khuff reservoirs offshore Abu Dhabi, with multiple appraisal wells drilled in different fields. Those wells were evaluated using detailed logging campaigns and then subjected to well tests, usually through drillstem testing for targeted intervals. The interpretation of well tests, combined with advanced petrophysical analysis, formation-test data, and production logs, provided insight into the nature of the Khuff reservoirs. A wide range of responses was observed, from tight to highly productive, but not necessarily with clear previous indications of deliverability or inflow intervals. Overview of the Khuff Formations The key characteristics of the Khuff formations offshore Abu Dhabi have been well-documented in previous work and can be summarized by the following: Low porosity and permeability carbonate reservoirs, where natural fractures are critical contributors to flow Properties vary widely laterally, with significant uncertainty regarding connectivity Variations in stress and petrophysical properties can be significant and affected by diagenetic and tectonic history These reservoirs present significant challenges for development planning. Previous studies have shown that it can be difficult to relate production performance to standard petrophysical analysis directly and that the presence of fractures - in particular, critically stressed fractures - in the vicinity of the wellbore is an essential factor for production performance. Productivity also was found to vary by several orders of magnitude within the same reservoir depending on the field and lateral location of a given well. The presence of natural fractures has been recognized as a major contributor to flow in tight gas reservoirs; however, this raises several questions related to assessing formation potential. First, the nature of the fractures must be evaluated. Some will contribute to production, while others will remain sealed. Equally importantly, identifying zones with promising porosity developments is not a solid indicator of production expectations. Well-Test Observations With more than 20 drillstem tests performed in the Khuff reservoirs during a 4-year period, the first observation is the wide range of reservoir responses encountered, with an apparent lack of consistency within a given reservoir or field.

Research of complex fracture acid fracturing technology in low permeability carbonate reservoir

Based on the analysis of the characteristics of the low-permeability carbonate reservoir, this paper has carried out studies on the adaptability of geological conditions, the adaptability of rock mechanics conditions and the characteristics of horizontal stress of the reservoir, etc., and demonstrated the feasibility of the acid fracturing of the low-permeability carbonate reservoir with complex fractures. In view of the poor connectivity of low-permeability carbonate reservoirs and the unsatisfactory effect of conventional acid fracturing, a “large scale + high pump rate” process idea of complex fracture acid fracturing technology was formed through the simulation of process parameters and optimization of construction pipe string, so as to expand the reservoir stimulation volume and improve the communication scope.

Ichnofabrics and their roles in the modification of petrophysical properties: A case study of the Ordovician Majiagou Formation, northwest Henan Province, China

The carbonate rocks of the Ordovician Majiagou Formation from the northwestern part of Henan Province and their diverse trace fossils provide an outcrop analogue for bioturbated, low porosity and low permeability carbonate reservoirs. Three ichnofabrics can be defined based on the ichnotaxonomy, ichnodiversities, individual abundances and overall bioturbation indices. The Balanoglossites ichnofabric represented medium to relatively high-energy sedimentary environments such as intertidal zone and subtidal shoals in the carbonate platform. The Planolites ichnofabric suggested relatively low-energy, protected sedimentary environments such as lagoons in a restricted carbonate platform, but also occurs in the deeper-water depressions in the open carbonate platform. The Chondrites ichnofabric characterized low-energy and reduced oxygen conditions, with burrows penetrating below a shallow redox interface in deeper-water settings that include the distal slope as well as local depressions in the open carbonate platform. Our analysis suggests that the modifications of petrophysical parameters are controlled or influenced by the ichnofabric characteristics (burrow architecture, burrow cross-cutting relationship, degree of bioturbation and burrow connectivity), the permeability contrast between burrow-fills and matrix, and the dolomitization of burrow-fills. The micritic host sediments have negligible porosity and permeability. The bioturbated sediments have significant differences in fabric, texture, porosity and permeability. The burrow-fills are very commonly dolomitised. Each of the burrow-types can enhance reservoir properties, but the greatest differences are associated with the Balanoglossites ichnofabric, in which porosity and permeability can reach over 2% and 70 mD. Preliminary stratigraphic studies indicate that reservoir potential in the subsurface will be linked to an understanding of the distribution of shoal and intertidal facies as well as their associated Balanoglossites ichnofabrics.

A New Drilling Fluid System for Temporary Plugging of Low Permeability Carbonate Reservoirs Containing H2S and CO2

Carbonate reservoirs in Asmari block of Missan oilfield are characterized by complex geological characteristics. Temporary plugging is commonly used for effective damage control caused by the working fluid when drilling in such complex multi-pressure reservoirs and for preventing leakage, blowout, and collapse in long open hole wells. In this paper a new temporary plugging drilling fluid formulation system is proposed, designed for the low permeability carbonate reservoir containing H2S and CO2 in the target block. The system contains a specially designed acid-soluble temporary plugging agent DSK-1 combined with HCOONa and modified biopolymer VS-A flow pattern regulator; modified starch water loss reduce agent, and DSP antioxidant. The parameters of the system, including temperature stability, resistance to formation water pollution, inhibition capacity, and stability to acids and acid gases, were evaluated in a series of experimental tests. The plugging ability was evaluated by experiments on cores received from the target block. The results showed that the plugging removal and recovery rate of the drilling fluid system was up to 86.3%, which can effectively meet the target requirements, and provide guidance and reference for the follow-up study of temporary plugging in low permeability carbonate reservoirs.

Accurate determination of permeability in carbonate reservoirs using Gaussian Process Regression

The knowledge of reservoir permeability determination is increasingly important in petroleum engineering. Understanding the alteration of the abovementioned parameter as a dynamic characteristic of formations helps the characterization of reservoir rock. The existing models available for permeability determination are mainly developed for sandstone reservoir; therefore, a great challenge is in the prediction of permeability in carbonate heterogeneous rock. The current study addresses the application of Gaussian Process Regression (GPR), a state-of-the-art machine learning algorithm, in estimating permeability of carbonate reservoirs. This Bayesian network offers various benefits including non-linearity and multi-dimensionality. A widespread source of data pertinent to four deposits of Vuktyl'skiy, central Asia, Kuybyhev, and Orenburg was adopted from literature. The data set includes water saturation, effective porosity, and pore specific surface area parameters in association with absolute rock permeability as the target. Four different Kernels of Matérn, rational quadratic, exponential, and squared exponential are utilized as the covariance functions of the GPR network. A wide variety of the visualizations and statistical parameters are prepared to assess the potential of the established models in permeability estimation. Finally, it is demonstrated that the GPR (Matérn) gives the highest precision with Mean Magnitude Relative Error (MMRE) and Adjusted R-squared equal to 38% and 0.98, respectively. Besides, the applied sensitivity analysis reveals that porosity and irreducible water saturation have the lowest and the highest absolute impact values on permeability estimation. The applicability and validity of the developed models are also demonstrated by means of the so-called Williams' method for outliers detection. At last, it is worthwhile mentioning that the created new methods in this study can be good nominees for permeability determination in characterizing the heterogeneous carbonate reservoirs.