The accurate prediction of water saturation in reservoir exploration and development remains a significant challenge, particularly in regions like the Middle East with complex carbonate formations such as the Mishrif Formation. While geophysical logging data is widely utilized for this purpose, however, the complex pore structures render Archie's formula unsuitable, leading to non-Archie phenomenon in rock-electrical experiments. Although the electrical efficiency model has been employed in calculating water saturation in carbonate reservoirs, there has been no prior study incorporating electrical porosity for its refinement. This study enhances the conventional electrical efficiency model by introducing the concept of electrical porosity. The improvement aims to mitigate the impact of isolated mold pores and sparse regions of current density distribution on electrical efficiency, focusing on the Mishrif Formation and quantitatively computing water saturation. Initially, the study area is categorized into three distinct rock-physics types of reservoirs using the Winland R35 method, with respective electrical porosities calculated. Subsequently, these results are integrated with the enhanced electrical efficiency model, and trial calculations are performed using geophysical well-logging data, followed by a comparison with core data. The findings reveal that the improved electrical efficiency model yields an average relative error of only 10.36 % compared to core data, whereas the respective errors for Archie's formula and traditional electrical efficiency models are 17.65 % and 20.92 %, indicating enhanced accuracy with the improved approach. Across different reservoir types, a decrease in electrical porosity proportion is observed with diminishing pore-throat radius. Additionally, the consistency of this trend is validated by nuclear magnetic resonance logging data. Lastly, the necessity of reservoir rock-physics type classification for electrical porosity computation is confirmed. For heterogeneous reservoirs, direct calculation of electrical porosity is infeasible, thus underscoring the essential groundwork of reservoir rock-physics type delineation. This study improves water saturation prediction accuracy and applicability by introducing electrical porosity to refine the conventional electrical efficiency model, holding significant implications for the exploration and development of complex reservoirs.

To address the challenge of identifying water-flooded layers in the high-porosity, high-permeability, and strongly heterogeneous reservoirs of the Guantao Formation in the Penglai 19-3 Oilfield, research on water-flooded layer identification methods was systematically conducted. The logging characteristics of oil layers and water-flooded layers at different levels overlap considerably, which limits the accuracy of traditional identification methods. Meanwhile, the Archie equation shows significantly reduced applicability during the moderate and strong water-flooding stages. A water-flooded layer identification model was constructed using HistGBDT, and performance comparison between the base model and the optimized model reveals that the latter achieves a test accuracy of 91.6%. Compared with BPNN and SVM, the optimized HistGBDT model demonstrates substantially higher test accuracy and better generalization performance. Based on six sets of logging data, the optimized HistGBDT model developed enables the accurate identification of oil layers and multi-level water-flooded layers. It provides a reliable technical approach for tapping remaining oil in the high-water-cut stage of the Penglai 19-3 Oilfield and offers a new method and engineering reference for water-flooded layer identification in similar high-porosity, high-permeability heterogeneous reservoirs in the Bohai Bay Basin.

Accurate evaluation of coalbed methane (CBM) content is crucial for effective exploration and development. Traditional gas content measurement methods based on laboratory analysis of drill core samples are costly, whereas geophysical logging methods offer a cost-effective alternative by providing continuous high-resolution profiles of rock layer physical properties. However, the relationship between CBM content and geophysical logging data is complex and nonlinear, necessitating an advanced prediction method. This study focuses on the No. 3 coal seam in the Shizhuang South Block of the Qinshui Basin, utilizing geophysical logging data and 148 sets of laboratory core samples. We employed the Random Forest (RF) method optimized with a simulated annealing-genetic algorithm (SA-GA) to develop the SA-GA-RF model for evaluating CBM content. The model's performance was validated using test data and new CBM well data, and it was applied to calculate the vertical gas content profiles of No. 3 coal seam across 128 wells. The SA-GA-RF model demonstrated an average relative error of 13.13% in the test data set, outperforming Backpropagation Neural Network (BPNN), Least Squares Support Vector Machine (LSSVM), Extreme Learning Machine (ELM), and multivariate regression (MR) methods. The model also exhibited strong generalizability in new wells and improved model-building efficiency compared to traditional cross-validation grid search methods. The construction of a three-dimensional CBM content model, incorporating well coordinates and elevation data, allowed for detailed identification of high gas content areas and layers. This three-dimensional model offers a more precise characterization than traditional two-dimensional isopleth maps, providing valuable insights for CBM exploration, reserve evaluation, and production optimization.

Petrophysical rock typing plays a major role in carbonate reservoir characterization and geological modeling for reservoir simulation. In this study, based on 1120 samples of mercury-injection capillary pressure data, we have analyzed the contributions to rock permeability for the thick Cretaceous carbonate reservoir of Mishrif Formation in H Oilfield in Iraq. The analyses have been carried out for three types of pore-throat frequency distribution, i.e., monomodal, bimodal, and trimodal. The results show that for the bimodal and trimodal samples, the pore-throat frequency distribution of the first pore system contributes more than 95% of rock permeability. Further, the Thomeer function has been used to fit the mercury-injection capillary pressure curve, in which the displacement pressure, pore geometry factor, and bulk volume have been extracted. These three parameters have been utilized to characterize the pore structure and to fit with the rock permeability so as to obtain the reservoir classification parameter Mode. Then, we have established the relationship between permeability and mode, permeability and R-35, and the correlation coefficients are 0.81 and 0.75, respectively. And permeability derived from Mode is more accurate than R-35. The Modes of 1120 samples have been arranged in ascending order and plotted on the semi-logarithmic scales. After interpolation, derivation, and filtering, five rock types have been divided at the change in slope. The combination of Mode and this slope method is considered an improved method. The classification results of Mode, Winland R-35, and Flow Zone Indicator have been compared with those of pore-throat distribution,T(2)distribution of nuclear magnetic resonance, and conventional logging responses. The results demonstrate that the classification results of Mode are significantly better than those of Winland R(35)and Flow Zone Indicator.

总有机碳含量(total organic carbon,TOC)是确定烃源岩质量的最重要参数.为解决传统ΔlogR方法应用于沉积环境较复杂的海相页岩气储层精度低的问题,以桂中坳陷A区B组海相页岩气储层为研究对象,在分析研究区低阻原因的基础上,对测井曲线按照扩径范围进行井径校正,并利用自然伽马能谱测井中Th/K数据,与ΔlogR建立多元拟合关系,提出基于井径校正和自然伽马能谱测井的改进ΔlogR方法.结果表明:声波时差曲线经过校正后,降低了井径扩径带来的测井数据失真的影响,同时结合自然伽马能谱测井,能够适应研究区复杂的地质背景和较强的非均质性,大大提高总有机碳质量分数的预测精度,具有较好的推广性和适用性.该方法可以为桂中坳陷A区B组海相页岩气储层评价提供参考.

The content of industrial components of coalbeds, one of the main parameters of coalbed methane (CBM) reservoirs, is crucial in the entire coal mine resource exploration and exploitation process. Currently, using geophysical logging data to determine the content of industrial components is the most widely implemented method. In this study, the PZ block in the Qinshui Basin was employed as a target block to evaluate ash (A ad ), fixed carbon (FC ad ), volatile matter (V daf ), and moisture (M ad ) under the air-dry (AD) base condition based on the autocorrelation between the geophysical logging curves and industrial component contents combined with the OBGM (1, N) model. The results indicate that 1) the geophysical logging curves combined with the OBGM (1, N) model can accurately predict the A ad and FC ad contents and an increase in geophysical logging curve types can effectively improve the model performance, compared to using a single geophysical logging curve for prediction. 2) When predicting the V daf content, using the geophysical logging curves combined with A ad and FC ad contents had the highest prediction accuracy. Further, prediction bias does not exist, compared to using only the geophysical logging curve or the autocorrelation between the industrial component contents. The entire evaluation process begins with an assessment of the A ad and FC ad contents. Then, the V daf content was assessed using the content of these two industrial components combined with geophysical logging data. Finally, the M ad content was calculated using the volumetric model. Accurate application results were obtained for the verification of new wells, demonstrating the efficacy of the method and procedure described in this study. 3) The OBGM (1, N) model has the highest prediction accuracy compared with the multiple regression and GM (0, N) models, which have the same computational cost. The geophysical logging interpretation model of the proposed coalbed industrial component contents is simple to calculate and suitable for small samples, providing a new method for the evaluation process of industrial component contents.

The pore structure of glutenite in Bozhong 19-6 Gasfield is complicated. In order to improve the estimation accuracy of permeability, it is necessary to start with the pore structure and find the pore structure factor with the best correlation with permeability. Taking the pore structure and permeability of 43 Kongdian Formation glutenites as the research object, using rock casting thin slices to determine the pore types, and obtaining pore-throat size distribution characteristics and pore structure parameters through high-pressure mercury intrusion. Combining pore types and pore structure parameters to analyze the relationship between pore structure and permeability, a permeability evaluation model based on pore structure parameters was established. Studies have shown that there are differences in the pore structure between different types of dissolved pores. The pore structure of dissolved pores in the grain is the best, and the pore structure of dissolved pores in the cement is the worst. The physical properties of glutenite with different types of dissolution pores vary greatly. The rock samples with intragranular dissolution pores and no cement dissolution pores have the best physical properties. Different pore structure factors have inconsistent degrees of permeability control. Among them, the permeability model which based on pore throat size, connectivity, ratio and shape has the highest accuracy. The large pore throat radius and good connectivity of the dissolved pores in the glutenite grains of the Kongdian Formation of BZ19-6 Gasfield are the main reasons for the good reservoir and seepage capacity of this kind of rocks. The average pore throat radius, mercury removal efficiency, average pore throat volume ratio and fractal dimension are suitable for estimating the permeability of glutenite reservoirs with complex pore structures and（extremely） low porosity and permeability, in order to provide technical support for the permeability evaluation of glutenite reservoirs inBozhong Depression, Bohai Bay Basin.

Reservoir lithology identification is an important part of well logging interpretation. The accuracy of identification affects the subsequent exploration and development work, such as reservoir division and reserve prediction. Correct reservoir lithology identification has important geological significance. In this paper, the wavelet threshold method will be used to preliminarily reduce the noise of the curve, and then the MKBoost-MC model will be used to identify the reservoir lithology. It is found that the prediction accuracy of MKBoost-MC is higher than that of the traditional SVM algorithm, and though the operation of MKBoost-MC takes a long time, the speed of MKBoost-MC reservoir lithology identification is much higher than that of manual processing. The accuracy of MKBoost-MC for reservoir lithology recognition can reach the application standard. For the unbalanced distribution of lithology types, the MKBoost-MC algorithm can be effectively suppressed. Finally, the MKBoost-MC reservoir lithology identification method has good applicability and practicality to the lithology identification problem.

The formation factor, which reflects the electrical conductivity of porous sediments and rocks, is widely used in a range of research fields. Consequently, given the discovery of numerous porous reservoir rocks and sediments exhibiting complex conductivity characteristics, methods to quantitatively predict the formation factor have been actively pursued by many scholars. Nevertheless, the agreement between the theoretically calculated and measured formation factors remains unsatisfactory, partially because the distribution characteristics of the entire pore space affect the final formation factor. In this study, a new method for characterizing the formation factor is proposed that considers the impacts of different complex pore structures on the conductivity of pores at different positions in the pore space. With this method, the electrical transmission through a rock can be accurately and quantitatively estimated based on the conductivity and shape of pores, the tortuous conductivity, and the classification of the pore space into conductive, weakly conductive, and nonconductive pores. By evaluating 24 datasets encompassing 7 types of rocks and sediments, including marine hydrate-bearing sediments and shale, the proposed model achieves remarkable agreement with the experimental data. These excellent confirmation results are attributed to the ubiquitous presence of weakly conductive and nonconductive pores in almost all rocks and sediments. Through further research based on this paper, an increasing number of adaptation models and a comprehensive set of evaluation methods can be developed.

Total organic carbon content is the important parameter in determining the quality of hydrocarbon source rocks. To accurately evaluate the TOC parameters of shale reservoirs and coal-measure shale reservoirs, the method to improve the accuracy of a reservoir TOC parameter calculation is investigated using the continental shale A1 well, the marine shale B1 well, and the marine-continental transitional shale C1 well as examples. Each of the three wells characterize a different paleoenvironmental regime. The ∆log R method based on natural gamma spectroscopy logging is proposed to calculate the TOC of shale reservoirs, and the dual ∆log R method based on natural gamma spectroscopy logging is proposed to calculate the TOC of coal-measure shale reservoirs. The results show that the proposed new method can reduce the absolute error by about 0.06~7.34 and the relative error by about 6.75~451.54% in the TOC calculation of three wells. The new method greatly expands the applicability of the ∆log R method and can effectively assist in the exploration and development of shale and coal-measure shale reservoirs.

Organic matter (OM)-hosted pores are the primary pore type in the deep shale reservoirs of the Longmaxi Formation, in the Sichuan Basin, China, as well as the main locations for shale gas enrichment. However, the quantitative characteristics of OM pore space are not clear, and there is a lack of characterization methods. In this study, scanning electron microscopy (SEM), mercury intrusion porosimetry, low temperature gas adsorption, and logging data are all used to carry out multi-scale quantitative characterization of OM-hosted pores. The shape factor (F) model, fractal dimension model, and the OM porosity model are improved by combining them with mathematical morphology. The study primarily examines the effect of heterogeneity and the development of full scale pores in the OM pore space of the Luzhou area. The results show that (1) the ideal shape factor (F*) reflects the compaction degree of OM-hosted pores. From macropores to micropores, F* ranges from 0.61 to 0.93 as the shape gradually changes from flattened to round. (2) Shape factor and fractal dimension analyses revealed that the heterogeneity strength of the nanopore structure is mainly controlled by macropores. At the micro level and macro levels, the OM space shows lateral homogeneity and vertical heterogeneity. (3) Compared to the established method, the relative error of the improved model used in this study for predicting OM porosity was reduced by 19%. (4) Compaction and pore structure heterogeneity influence the formation of nanoscale pores in the Luzhou region and these factors also promote the development of mesopores and micropores, respectively. Furthermore, the OM porosities of the dominant shale reservoirs in Luzhou account for more than 50% of the overall porosity. This understanding will be of considerable benefit for evaluating deep shale reservoirs.

Hydrothermal fluid is one of the factors controlling Archean buried hill reservoirs in Bozhong 19-6. However, there are no clear studies focusing on the influence of hydrothermal alteration products and their lithological characteristics on reservoirs. Through characterization of the alteration reservoir and construction of a new subtraction model of the logging-rock mechanical alteration degree, the comprehensive uses of core, thin section, and electrical imaging logging data are considered as the research objects with metamorphic and igneous rocks. Thus, the relationship between lithologies with different alteration degrees and reservoir quality is revealed. The study shows that feldspar chloritization and sericitization are the main factors controlling the hydrothermal alteration of the reservoir; the overall alteration degree of igneous rocks is high, and the overall alteration degree of metamorphic rocks is low; the reservoir with strongly altered igneous facies is prone to forming dissolution pores, with strong reservoir inhomogeneity and poor reservoir performance (alteration degree is greater than 15%); the reservoir with weakly altered metamorphic facies is prone to developing fractures and a high reservoir productivity (alteration degree is 0); The reservoirs with altered metamorphic facies are numerous in the formation, spatially diverse in type, and second in reservoir quality only to those in the weakly altered metamorphic facies (alteration degree of 0–15%). This method is expected to provide a reference for quickly finding advantageous reservoirs in the Bohai Sag.

In order to accurately evaluate the shale reservoir of the Lower Carboniferous Luzhai Formation in the central Guangxi area, in view of the influence of the low resistivity of the reservoir and the complex mineral composition on the logging response, taking Well A1 as an example, a method to improve the calculation accuracy of reservoir parameters is studied. The reasons for the low resistance in the study area are analyzed from the aspects of minerals, geochemistry and geology. An improved △log R method based on borehole correction and natural gamma spectroscopy logging was proposed to calculate the total organic carbon content. A calcium-corrected HERRON method is proposed to calculate porosity. The P/S time difference ratio-density neutron log overlap difference is proposed to calculate saturation. Calculation of adsorbed gas content based on organic matter correction. The results show that the improved method greatly improves the prediction accuracy, and has high consistency with the core analysis results, and can accurately evaluate the shale reservoirs of the Lower Carboniferous Luzhai Formation in the central area of Guangxi. The improved new method has good applicability and can be further used in the evaluation of this kind of source rock reservoir.

The accurate quantitative calculation of mineral components is very important and basic work in formation evaluation. Using well-log data to estimate mineralogy and porosity is a mainstream method with core measurements often used. However, in shale reservoirs, there are many mineral components, such as organic matter and pyrite. In addition, the pore structure is complex, and gas exists in the pores as free state, adsorbed state, and dissolved state. These factors make the logging response characteristics more complex and thus the estimation of the mineral components more difficult. To address this problem, we have adopted a mineral inversion method based on error analysis and response equation error. Based on the error analysis of the mineral inversion method, we first establish a technique to obtain interpretation parameters and the function of the response equation error combined with the core data. Then, based on the weighted total least-squares method, we construct the objective function, and we use the improved krill herd algorithm to solve the problem. Finally, we estimate the mineral component volume. The calculated results indicate that the method can accurately determine the clay, quartz + feldspar, carbonate contents, and porosity by conventional logging data. Compared to the traditional mineral inversion method, the average relative error is reduced by 11.1%. The method has high applicability to shale reservoirs and can supply the basic parameters for formation evaluation.

After more than seven years of commercial development in the Fuling shale gas field, most old wells in the main block are confronted with significant pressure decline, so shale gas production is lower than the critical fluid-carrying flow rate and considerably reduced production. Pressure-boosting stimulation technology can increase the pressure difference between flow pressure and transmission pressure, which improves gas transmission capacity, maintains production and subsequently improves the recovery. This study analyzes the pressure-boosting stimulation pattern and prediction method of the remaining recoverable reserves in the Fuling shale gas field. The findings show that the pressure-boosting stimulation pattern of a gas-gathering station can reduce the abandonment pressure of a gas well to the maximum extent and fulfill the requirements for the new adjustment wells in the gas-gathering station; therefore, it is a pressure-boosting pattern suitable for the Fuling shale gas field. Blasingame analysis method can effectively predict the production change of continuous recovery over the next ten years. In addition, the influence of water production in shale gas wells on pressure-boosting stimulation technology is discussed. The research results can provide experience and direction for the continuous improvement of gas recovery in the Fuling and the same type shale gas fields.(c) 2022 Elsevier Ltd. All rights reserved.

Accurate evaluation of coalbed methane (CBM) content plays a momentous role in the identification and efficient development of favorable exploitation blocks of CBM resources, but there are still many technical challenges in the exploration and development of onshore CBM fields. With the development and application of geophysical logging technology, using geophysical logging data to predict the gas content of CBM reservoirs has been proven to be an effective and feasible solution. However, the complex logging response of the CBM reservoirs makes it difficult to characterize the relationship between the gas content and the logging curve response by a simple linear relationship. In this paper, kernel extreme learning machine (KELM), a machine learning method, is combined with the geophysical logging data to predict the vertical variation curve of gas content in CBM wells. In this paper, the laboratory data on coal rock gas content from 12 CBM wells in the Southern Shizhuang block are selected, and a CBM content prediction model based on the KELM method is constructed by selecting the log curves, combining cross-validation and grid-seeking to determine the hyperparameters, and validating the prediction model using the test dataset and a new well in the same block. The application of the model on the test dataset was remarkable, and the vertical variation of CBM content obtained by applying it to the new well was consistent with the laboratory results, which proved the correctness and generalizability of the model. The results of this paper show that the CBM content evaluation model based on the KELM method and geophysical logging data is applicable to the 3(#) coal seam in the target block and can be used to predict the vertical CBM content of CBM wells; compared with the extreme learning machine (ELM) method and the backpropagation neural network (BPNN) method, the KELM method requires fewer hyperparameters to be explored when constructing the CBM content evaluation model, and the model construction is simple and has high prediction accuracy. At the same time, the CBM content model constructed by the KELM method differs for different blocks, coal seams at different depths, and different response ranges of geophysical logging data. The construction of a CBM content prediction model using the KELM method and logging curves is an effective means of characterizing CBM resources, and the model construction process and evaluation criteria studied in this paper can be used to help other blocks evaluate the CBM content, providing guidance for further exploration and development of CBM fields with practical application.

As a country rich in shale gas, China has developed relatively mature evaluation systems for marine shale exploration and conducted tentative developments for continental shale. However, limited research has been conducted on marine-continental transitional shale. To address this gap in the research from the perspective of well logging, we have used marine-continental transitional shale of the Longtan Formation in the southeast Sichuan Basin to compare the differences between Longtan shale and marine shale with respect to organic matter, lithology, and logging responses to identify marine-continental transitional shale reservoir features. We also have analyzed the "low-resistivity" genesis of marine-continental transitional shale. Studies on the Longtan shale have demonstrated that the combination of high clay content, complex pore structure, development of pyrite, graphitization of organic matter, and presence of high-rank coal seams collectively produces low-resistivity properties in the reservoir. Saturation is essential in petroleum exploration and development. However, the low-resistivity characteristics result in low accuracy of gas saturation when calculated by the Archie equation or its electric derivation equations. To improve the efficacy of saturation prediction, we compare and analyze existing nonelectrical models and develop a new process for calculating saturation based on the density-neutron combined model. The errors between the calculation results and core saturation are minor. In addition, the new calculation model exhibits a good application effect in marine shale in the Jiaoshiba area, providing a new approach for saturation evaluation of shale formations.

岩石导电性特征是储层流体性质评价的重要依据,岩电实验是研究岩石导电性的重要手段.但是,由于碳酸盐岩非均质性较强,难以获得具有不同角度和宽度裂缝的代表性岩心.为此,基于X-CT扫描的碳酸盐岩三维图像,利用分形布朗运动算法加入各种不同宽度和角度的裂缝,构建了含有裂缝的碳酸盐岩三维数字岩心模型,并利用数学形态法模拟了油水分布.在此基础上,利用有限元法开展了导电性模拟,研究了裂缝宽度对地层因素的影响、裂缝宽度对电阻率增大系数的影响及裂缝角度对储层导电性的影响.结果表明,随着总孔隙度的增加,平行于裂缝方向(X方向、Y方向)的胶结指数降低、饱和度指数增加,而垂直于裂缝方向(Z方向)的胶结指数升高、饱和度指数减小;裂缝角度对储层导电性影响较大,导致储层各向异性较强:X方向的地层因素随着裂缝角度的增加而增大;Y方向的地层因素变化幅度较小;Z方向的地层因素随着裂缝角度的增加而减小.研究结果为裂缝性碳酸盐岩储层的勘探提供了重要的参考依据.

靖西地区马家沟组气水关系复杂,不同流体间的地球物理测井响应差异微弱,给传统测井解释工作带来了困难.以地球物理测井、薄片、扫描电镜、压汞等资料为基础,探究了储集空间结构以及地层特征对高阻水层响应特征的影响,深入分析了高阻水层成因,建立了常规地球物理测井与电成像测井资料相结合的随机森林流体识别模型.结果表明,靖西地区马家沟组储层复杂的孔隙结构和广泛发育的薄互层是致使水层地球物理测井响应表现为高阻特征的主要因素;建立的随机森林流体识别模型能有效地解决高阻水层问题,判别精度达到84％,并在验证盲井中表现稳定.研究结果有效地提高了该地区的测井解释符合率,为后期油藏评价与勘探开发奠定了基础.

The study of conduction mechanisms is the key to establishing physical derivations, resistivity simulations and saturation models. The purpose of this research is to clarify conduction mechanisms under different diagenetic facies and build suitable saturation evaluation models. Experimental data of tight gas sandstone from the Ordos Basin were analysed, including data from scanning electron microscopy, conventional core physical property analysis, core casting thin-section analysis, core mercury intrusion experimentation and rock electrical conductivity experimentation. Accordingly, the diagenetic minerals of the study block were examined, and the diagenetic facies were classified by the differences in the diagenetic properties across the study area. The reservoir was divided into three types of diagenetic facies: construction facies, cementation facies and destruction facies. On this basis, the conductivity characteristics and saturation models of different diagenetic facies within the study area were systematically discussed for the first time. A number of experiments showed that according to the type of diagenesis, the structure of the pores and the influence of the reservoir, a classification scheme for diagenetic facies (consisting of construction, cementation and destruction facies) can be established. According to the influence of the diagenesis of various diagenetic facies, theoretical pore structure models of the three diagenetic facies were established, in which the construction facies includes mainly dissolved feldspar pores and intergranular pores, the destruction facies includes clay residual intergranular pores and intergranular pores, and the cementation facies includes primarily residual intergranular pores. Based on these theoretical pore structure models, the construction facies was evaluated with a pore-connected vuggy conductivity model, the destruction facies was evaluated with a non-connected matrix pore conductivity model, and the cementation facies was evaluated with a residual intergranular pore conductivity model. Then, the rationality of each model and the effects of the parameters in each model on the final cementation exponent were analysed by simulation. The predicted cementation exponents of the diagenetic facies match the measured cementation exponents well and can guide the qualitative description of these characteristics in such reservoirs.