Mercury Injection Capillary Pressure Curves Research Articles

A New Multi-Scale Method to Evaluate the Porosity and MICP Curve for Digital Rock of Complex Reservoir

The evaluation of rock porosity and the mercury injection capillary pressure (MICP) curve is fundamental for oil and gas exploration and production. Digital rock (DR) technology, incorporating 3D micro-CT imaging and numerical methods, has been widely employed to predict these properties. However, analyzing the pore structure of heterogeneous rocks, such as fractured rocks or glutenite, solely through single-scale DR analysis poses challenges. Existing upscaling methods have limitations in fully representing the complete range of pore structures at different scales, with limited comparison to experimental data. To address this, we propose a novel method that upscales porosity and simulates the MICP curve from nano-scale to core scale by merging results from micro-CT (at resolutions of 35 μm and 2 μm) and SEM (at resolutions of 6.5 nm and 65 nm). We validate the developed DR model by applying it to sandstones, glutenite, and igneous rocks, and achieve excellent agreement between the experimental data and the multi-scale DR model across 67 samples. The results demonstrate that the multi-scale model effectively captures the porosity and pore structures across the entire range. In contrast, the single digital rock (DR) model underestimates the porosity measurements for both homogeneous sandstones and heterogeneous cores. While the MICP model based on a single DR proves suitable for homogeneous rock samples, it introduces noticeable discrepancies when applied to heterogeneous rock samples. The developed multi-scale method significantly enhances the confidence in using DR to assess the pore structure of complex rocks.

A Method for Evaluating Reservoir Permeability Based on Machine Learning Flow Unit Index

Abstract The H formation of the Y gas field in the X depression belongs to a low-permeability tight sandstone reservoir affected by sedimentation, diagenesis, and cementation. The lithology and pore structure of the target layer are complex, with strong physical heterogeneity and complex pore-permeability relationships. Conventional core pore permeability regression and nuclear magnetic resonance software-defined radio methods do not satisfy the requirements for precise evaluation in terms of permeability calculation accuracy. Based on the principle of the flow zone index (FZI) method, this study analyzed the influence of pore structure on permeability and extracted three pore structure characterization parameters, namely, the maximum pore throat radius (Rmax), displacement pressure (Pd), and average throat radius (R), from the mercury injection capillary pressure curve. The relationship between the FZI and pore structure is clarified. Therefore, the FZI in this area can characterize the permeability differences within different flow units. Based on the flow unit theory, a refined evaluation model for three types of reservoirs was established in the study area. By analyzing the response characteristics and correlation of conventional logging curves using machine learning, three optimization combination curves were selected, and a multiparameter fitting equation for the FZI was established, which was applied to predict the permeability of new wells. The results showed that the calculated permeability was highly consistent with the core analysis results, thereby providing a theoretical basis for the precise evaluation of low-permeability tight reservoirs.

Evaluation of irreducible water saturation by electrical imaging logging based on capillary pressure approximation theory

One of the most important metrics for evaluating oil and gas reservoirs is irreducible water saturation (Swir). The completion of oil and gas reservoir development tasks, such as fluid identification, productivity prediction, and water-flooded reservoir discrimination, requires the accurate evaluation of Swir. Nuclear magnetic resonance (NMR) logging can reflect the intricate pore structure of reservoirs, so it offers a natural advantage in the evaluation of Swir, but its high measurement cost leads to infrequent use. In geophysical logging, electrical imaging logging can obtain pore size distribution information at a lower cost. Hence, a method based on electrical imaging logging data is proposed to predict Swir. This method not only has a high degree of accuracy but is also cost-effective. First, the pore characteristics of the bound fluid were analyzed with the mercury injection capillary pressure curve. Based on the capillary pressure approximation theory, the reverse cumulative curve of the porosity spectrum was derived. And the physical meaning of its “inflection point” was clarified. Then, the resistivity of the formation containing just irreducible water was computed using the conversion between the core displacement pressure and resistivity. Finally, based on Archie's formula, the Swir at each depth of the reservoir was predicted. By using ultradeep carbonate reservoir logging data from the Yuanba gas field, the model was validated. The results demonstrated that the calculated values using this method agreed with the measured values from the cores. Compared with the core fitting method, the average absolute error is reduced by 2.61%, and the average relative error decreases by 12.0%. If NMR logging information is not present, this method serves as an effective supplement to accurately predict Swir.

Investigation and prediction of pore type system by integrating velocity deviation log, petrographic data and mercury injection capillary pressure curves in the Fahliyan Formation, the Persian Gulf Basin

Investigation and prediction of pore type system by integrating velocity deviation log, petrographic data and mercury injection capillary pressure curves in the Fahliyan Formation, the Persian Gulf Basin

Novel Method for Predicting Mercury Injection Capillary Pressure Curves of Tight Sandstone Reservoirs Using NMR T2 Distributions

Novel Method for Predicting Mercury Injection Capillary Pressure Curves of Tight Sandstone Reservoirs Using NMR <i>T</i>₂ Distributions

Distribution characteristics of movable fluid in tight sandstone based on nuclear magnetic resonance

Movable fluid parameter is an important index to evaluate the seepage characteristics of tight sandstone reservoir. Combined with high-pressure displacement and nuclear magnetic resonance movable fluid testing principle, the nuclear magnetic resonance movable fluid characteristics of six typical tight sandstone core samples of Xujiahe Formation in Central Sichuan Basin were studied. Based on mercury injection capillary pressure curve, the nuclear magnetic resonance T2 spectrum distribution was converted into pore throat According to the radius distribution, the minimum pore throat radius of the movable fluid and the contribution of different pore sizes to the movable fluid are determined. The research shows that the movable fluid saturation of the tight sandstone reservoir in the second member of Xujiahe Formation in Central Sichuan is closely related to the pressure difference, with good logarithmic correlation, and the main contribution to the movable fluid saturation is the reservoir space > 0.1 μm. 70% of the total movable fluid saturation is injected at a lower pressure (about 5MPa). In the later stage, high pressure injection can only enter the reservoir pore with small pore size, and the total amount is small and increases slowly. Therefore, the total contribution of high pressure drive to gas saturation in the later stage is small.

A new method for predicting capillary pressure curves based on NMR echo data: Sandstone as an example

The mercury injection capillary pressure (MICP) curves have been widely used for the evaluation of the pore structure. In practical application, the MICP curves are usually obtained by the mercury injection experiment, but the experimental measurement can not obtain the MICP curves of the reservoir continuously. Since the pore size distribution obtained from the nuclear magnetic resonance (NMR) transverse relaxation time (T2) distribution is related to the pore-throat size distribution obtained from the MICP curve, so in previous studies, the MICP curves were predicted based on the NMR T2 distribution. However, the NMR T2 distribution obtained by the inversion of the NMR echo data has uncertainty, which affects the prediction accuracy of the MICP curves. In this paper, a new method for predicting the MICP curves of sandstone based on NMR echo data is proposed for the first time. Multiple characteristic parameters of the NMR echo data are calculated. The relationship between the parameters and the mercury saturation (Shg) is established at each capillary pressure point to predict the MICP curves. And the parameters of the MICP curves and the physical parameters are selected to create a reservoir classification index. The results show that the method for predicting the MICP curves of sandstone based on NMR echo data has high prediction accuracy and strong stability, and the reservoir classification index provides an accurate classification of sandstone reservoirs.

An innovative image-based workflow for quantitative pore structure characterization in reservoir rocks

Pore connectivity is a key parameter to affect reservoir quality (e.g. permeability). Routinely, this parameter is assessed with special core analysis (i.e. mercury injection capillary pressure analysis). In this paper, an innovative image-based pore structure workflow is proposed to quantitatively delineate the connectivity of pore space of reservoir rocks by using widely available thin section images. By borrowing the concept of mercury injection capillary pressure (MICP), this workflow is able to generate pseudo MICP curves that can be used to quantitatively characterize pore throat/body size distribution and pore connectivity of pore networks. This workflow consists of four steps: (1) HSV threshold-based pore identification; (2) K-D tree structure-based pore segmentation; (3) enhanced watershed-driven pore throat detection, and (4) pore throat distribution generation derived with a media-axis-maximal-circle algorithm. The pseudo MICP curves generated by the workflow can be used to quantitatively assess the variations in pore size, pore throat size, pore geometrical complexity as well as the density of pores of reservoir rocks. Two connectivity indices derived from pseudo MICP curves are introduced to evaluate the connectivity of pore network of reservoir rocks. This workflow provides an innovative and efficient tool for geologists and reservoir engineers to perform quantitative reservoir rock quality evaluation by utilizing widely-available thin section images.

A new model to estimate permeability using mercury injection capillary pressure data: Application to carbonate and shale samples

Estimating permeability of carbonate rocks using mercury injection capillary pressure (MICP) data has been carried out by many researchers in the past few decades. However, a major issue with almost all of the existing models is that they focus on a single aperture value from the capillary pressure curve. This study builds a new model to extract permeability from the entire pore throat sizes. Fermic-Dirac function was applied to fit the MICP curve to obtain some critical parameters such as R1 (the large curvature value) and R2 (the small curvature value). Afterwards, the partial least squares regression method was employed to develop a new permeability model. To verify the new model and check other models, we studied ten carbonate rock samples from an Iranian oil reservoir. The results showed that the R1 values vary from 1.00 to 2.73 while R2 values are found between 0.23 and 1.00. The new model performed better than the published models. The idea of building the model for the carbonates can be used in developing the permeability estimating model for shale samples, which could be a new model for the shale permeability estimation.

Characteristics and quantitative evaluation of volcanic effective reservoirs: A case study from Junggar Basin, China

The Permian Jiamuhe Formation in Jinlong-2 Block is an unconventional volcanic reservoir that has been a major target of oil production in the northwest margin of the Junggar Basin, China. This study focuses on analyses of geological, well-logging and production data to understand the characteristics and to set up quantitative evaluation method on volcanic reservoirs. The geological data reveals that the lithology mainly includes andesitic (welded) breccia, andesitic/basaltic brecciated lava and andesite. The reservoir space is mainly composed of stomata and dissolution pore, followed by intercrystalline pore and intergranular pore, therefore is characterized by low to medium low porosity (ϕ) and ultra-low to low permeability (k). The mercury injection capillary pressure curves have shown that the microscopic pore and throat of effective reservoirs mainly express in fine pore type with unimodal coarse state, partial coarse state and bimodal partial coarse state. Moreover, limits (ϕ ≥ 8%, k ≥ 0.03 × 10-3μm2) of effective volcanic reservoirs have been determined by oil test and cross plot of porosity and resistivity. Thus, based on the analysis of macroscopic and microscopic characteristics, we optimize the K-means clustering analysis method and typical Fisher's discriminant method to set up quantitative classification and evaluation criteria and discriminant functions, which divide the volcanic effective reservoir into 3 classes accordingly. Our results have been applied to the development test area, the average daily oil production of deployed horizontal wells is 204 bbl/d, and the average cumulative oil production for one year is 68 Mbbl. This quantitative classification method may provide strong technical supports for the efficient development of volcanic reservoir.

Generating pseudo-capillary pressure curves from nuclear magnetic resonance (NMR) data: a case study from the Cooper Basin, Australia

This study evaluates the accuracy of generating pseudo-capillary pressure curves from laboratory nuclear magnetic resonance (NMR) analysis and NMR wireline log data by comparing threshold pressures and curve characteristics to rock-derived mercury injection capillary pressure (MICP) analysis. Accurate production of pseudo-capillary pressure curves from NMR data could negate or reduce the need for expensive and rig-time-consuming core acquisition and subsequent MICP analysis. Successful application of this technique will allow production of pseudo-capillary pressure curves for large stratigraphic intervals at a significantly reduced cost and help to better constrain petroleum reserves and improve near field exploration prediction outcomes. The Tindilpie-11 well in the Cooper Basin, Australia, was used as a case study because of extensive existing conventional ore analysis and presence of an NMR wireline log covering the sampled intervals in the Permian-aged Patchawarra Formation. Six core samples underwent MICP and laboratory NMR analyses, with capillary pressure curves generated for each method. Six pseudo-capillary pressure curves were also generated from the NMR wireline log data at the coincident sample depths. The pseudo-capillary pressure curves generated from laboratory NMR data closely resemble the MICP results (mean threshold pressure error of 0.63 MPa absolute [91.4 psi]) and proved the applicability of this approach. The NMR wireline log data used is an average over a ∼1.6 m interval, which has likely led to discrepancies when compared with laboratory NMR pseudo-capillary pressure curves and MICP curves. Nonetheless, this study confirms the applicability of using laboratory NMR data to generate pseudo-capillary pressure curves that resemble MICP results. Continued development of NMR log technology and reduced logging speeds will improve the data resolution and ultimately provide high density NMR data that can be used to create pseudo-capillary pressure curves for large stratigraphic intervals at a significantly reduced cost. KEY POINTS Pseudo-capillary pressure curves derived from NMR data. Reservoir characterisation requiring minimal rock acquisition for ground truthing. Case study on the Patchawarra Formation (Cooper Basin, Australia) using SCAL data.

A method to evaluate pore structures of fractured tight sandstone reservoirs using borehole electrical image logging

Fractures are important in improving tight sandstone pore connectivity and fluid flow capacity. However, the contribution of fractures to pore connectivity and fluid flow capability cannot be quantified using current methods. The objective of this study was to develop a method to quantitatively characterize reservoir improvement as a result of fractures. This method was proposed based on the experimental results of 37 core samples recovered from the Upper Triassic Chang 8 tight sandstone in the Jiyuan area of the Ordos Basin, China. The morphological features of the porosity spectra, the mercury injection capillary pressure curves, and the pore-throat radius distributions were analyzed. Accordingly, a method was proposed to construct pseudocapillary pressure (Pc) curves from the porosity spectra to characterize the pore structure of fractured reservoirs and to establish corresponding models based on the classified power function method. In addition, a model was developed to predict fracture formation permeability based on the Swanson parameter. The proposed method and models were applied in field applications, and the estimated results agreed well with the core and drill-stem testing data. Fractured formations contained good pore structure, high permeability, and oil production rata. The relative errors between the model-predicted pore structure evaluation parameters, permeability from Pc curves, and core-derived results are all within ±30.0%. With the integrated study of reservoir pore structure with permeability, the effective Upper Triassic Chang 8 fractured tight sandstone reservoirs with development potential were successfully identified.

Predicting oil saturation of tight conglomerate reservoirs via well logs based on reconstructing nuclear magnetic resonance T2 spectrum under completely watered conditions

Abstract Due to complex lithology, strong heterogeneity, low porosity and permeability; resistivity logging faces great challenges in oil saturation prediction of tight conglomerate reservoirs. First, 10 typical core samples were selected to measure and analyse the porosity, permeability, nuclear magnetic resonance (NMR) T2 spectrum and mercury injection capillary pressure (MICP) curve. Second, an empirical method was proposed for reconstructing the NMR T2 spectrum under completely watered conditions using MICP curve based on the ‘three-piece’ power function. The parameters of different models were calibrated via experimental data analysis, respectively. The 180 core experimental data from an MICP curve were used as the input database. Porosity and permeability were regarded as the MICP data selection criteria to apply this model in formation evaluation. The comparison results show good application effects. Finally, to reflect oil saturation, the ratio of T2 geometric means of NMR T2 spectra under oil-bearing and completely watered conditions was proposed. Then, the quantitative relation between oil saturation and the proposed ratio was established via experimental data from the sealed cores, which established a quantitative prediction on oil saturation of tight conglomerate reservoirs. This showed a good application effect. The average relative error and the root mean square error (RMSE) of the predicted oil saturation and sealed coring measurement were around 10 and 3%, respectively. As the proposed method is only influenced by the wettability of reservoir and viscosity of oil, it is not only appropriate for the studied area, but also for other water-wet reservoirs containing light oil. It is important for identifying oil layers, calculating oil saturation and improving log interpretation accuracy in tight conglomerate reservoirs.

Nuclear magnetic resonance characterization of porosity-preserving microcrystalline quartz coatings in Fontainebleau sandstones

The almost-pure quartz–cemented Fontainebleau Formation (Paris Basin, France) sandstones are known to preserve their porosity because of microcrystalline quartz coatings. Here, we use nuclear magnetic resonance (NMR) techniques, petrography, scanning electron microscopy, porosity and permeability measurements, hysteresis, and mercury injection capillary pressure curves to identify and analyze their porosity structure. Nuclear magnetic resonance experiments include transverse relaxation time (T2) distributions and T2-filtered T2–T2 exchange (T2F-TREx), a technique that provides estimates on the diffusion coupling by comparing the evolution of families of pores in T2 distributions at different exchange times. Samples were divided according to their texture, composition, and abundance of microcrystalline quartz crystals, comprising group 1 samples with very low amounts of coatings and group 2 samples with entire grains coated by microquartz. Both groups show three (or four) peaks in NMR 2-MHz T2 distribution at approximately 1 (peak A), 10−1 (peak B), and 10−2 s (peak C); group 2 samples present a slight shift to shorter T2 times in comparison with other samples. The longest T2 peak A is because of intergranular macropores, whereas the shortest peak C is because of the microporosity associated with the microcrystalline quartz coating at the surface of the pores. Peak B is also because of microporosity associated with microcrystalline quartz, but with a different surface/volume ratio being likely related to flat-shaped pores within the microcrystalline coating. The T2F-TREx indicates the proton exchange is higher between macropores and the pore surface micropores (peak C) than between macropores and the internal flat-shaped micropores; no exchange between the two sets of micropores can be observed. Our results show the potential of NMR techniques in characterizing the microporosity in Fontainebleau sandstones, which is key for the mechanism of porosity preservation in these rocks.

Petrophysical static rock typing for carbonate reservoirs based on mercury injection capillary pressure curves using principal component analysis

Rock typing lay the profound basis of rational and efficient development of oil and gas reservoirs. In carbonate reservoirs, however, the identification of petrophysical static rock type (PSRT) has always been a great challenge to reservoir engineers and geologists due to its strong heterogeneity arising from the diagenetic process. The objective of this study was to propose a new method for petrophysical static rock typing in carbonate reservoirs. First, the evaluation equation of mercury injection capillary pressure (MICP) curve was established based on parameters analysis, and parameters weight was assigned by using the entropy method. Then, by analyzing and selecting the key influential parameters regarding the PSRTs in carbonate reservoirs, the novel rock typing model was established by principal component analysis (PCA) and ordinary least square (OLS) method. Finally, a realistic field case in Mishrif formation was presented through comparing the results from the proposed model with that of the existing methods, such as Winland plot (r35), flow zone indicator (FZI), FZI-star (FZI*) as well as bulk volume water method (BVW). The results indicated that the PSRTs identified by the new proposed model were superior to the others. What is reflected in the proposed model is that this approach can directly identify the PSRTs of the target formation. In contrast, neither the method of FZI nor the r35 can independently identify the PSRTs of carbonate reservoirs, they have to work jointly to determine the PSRTs.

An effective approach to generate drainage representative capillary pressure and relative permeability curves in the framework of reservoir electrofacies

Representative capillary pressure and relative permeability curves for reservoir rock types are important inputs for a dynamic reservoir simulator. The current study proposes a third-step approach to generate representative capillary pressure and relative permeability curves of reservoir rock types from nuclear magnetic resonance (NMR) log. First, reservoir electrofacies were identified by integration of conventional well logs and NMR log parameters. Employing cluster analysis reservoir rocks were classified into six electrofacies (EF) with reservoir quality being increased from EF1 to EF6. In the second step, the NMR T2-distribution arrays were converted to synthetic drainage capillary pressure curves and the results were validated by available laboratory measured mercury injection capillary pressure curves (MICP). Finally, the relative permeability curves were generated from the NMR-derived MICP data by using the Wyllie and Gardner equations for each individual electrofacies. Comparing the results with laboratory data indicates the effective role of electrofacies control in generating high accuracy capillary pressure and relative permeability curves. The results of this study show that the application of NMR log can further be extended through the generation of drainage capillary pressure and relative permeability data in the framework of electrofacies clustering.

F63IDENTIFICATION OF PERIPHERAL miRNA BIOMARKERS IMPLICATED IN THE DEFAULT MODE NETWORK

Representative capillary pressure and relative permeability curves for reservoir rock types are important inputs for a dynamic reservoir simulator. The current study proposes a third-step approach to generate representative capillary pressure and relative permeability curves of reservoir rock types from nuclear magnetic resonance (NMR) log. First, reservoir electrofacies were identified by integration of conventional well logs and NMR log parameters. Employing cluster analysis reservoir rocks were classified into six electrofacies (EF) with reservoir quality being increased from EF1 to EF6. In the second step, the NMR T2-distribution arrays were converted to synthetic drainage capillary pressure curves and the results were validated by available laboratory measured mercury injection capillary pressure curves (MICP). Finally, the relative permeability curves were generated from the NMR-derived MICP data by using the Wyllie and Gardner equations for each individual electrofacies. Comparing the results with laboratory data indicates the effective role of electrofacies control in generating high accuracy capillary pressure and relative permeability curves. The results of this study show that the application of NMR log can further be extended through the generation of drainage capillary pressure and relative permeability data in the framework of electrofacies clustering.

Pore - Throat size distribution indices and their relationships with the petrophysical properties of conventional and unconventional clastic reservoirs

Mercury injection capillary pressure (MICP) curves provide significant information about the pore size distribution (PSD), which controls and defines the main petrophysical properties, namely porosity, and permeability. Due to the importance of the PSD, several indices and parameters were introduced to quantify this property. 140 MICP curves for conventional and unconventional clastic reservoirs, representing different geographic localities, geologic ages, and depositional environments, were used to study these parameters and indices, as well as their relationships with porosity and permeability. This study indicated that the most popular indices, namely Thomeer pore geometrical factor (Fg) and Brooks and Corey pore size distribution index (λ), have poor relationships with both porosity and permeability. On the other hand, displacement pressure and Swanson parameter, and pore-throat radius corresponding to the 25th percentile of mercury saturation give the best relationships with them. Therefore, permeability prediction equations based on Thomeer and Brooks and Corey indices were modified. Additionally, several new equations relating PSD parameters and indices to porosity and permeability were introduced.

A New Method for Predicting Capillary Pressure Curves Based on NMR Logging in Tight Sandstone Reservoirs

The accurate prediction of capillary pressure curves is of great significance for the evaluation of pore structure in tight sandstone reservoirs. In this paper, a hybrid model is proposed to predict the mercury injection capillary pressure (MICP) curve using multiple characteristic parameters of the nuclear magnetic resonance (NMR) transverse relaxation (T2) distribution; models based on the capillary pressure point and on the non-wetting phase saturation point are combined. Because of the high level of multicollinearity among the characteristic parameters of the NMR T2 distribution, the partial least squares method is used to solve the model coefficients. The leave-one-out cross-validation (LOOCV) method is used to determine the optimal combination of parameters of the hybrid model, i.e., the number of latent variables of the model based on the capillary pressure point, the number of latent variables of the model based on the non-wetting phase saturation point, and the location of the splicing point. The result of model self-testing, the result of the LOOCV, and the analysis of the generalization ability all indicate that the hybrid model has high accuracy and strong stability for MICP curves prediction. The prediction results in tight sandstone reservoirs further verify the effectiveness of this method.

Permeability prediction from mercury injection capillary pressure curves by partial least squares regression method in tight sandstone reservoirs

Permeability is an essential petrophysical parameter for reservoir modeling, reservoir classification, and productivity prediction in tight sandstone reservoirs. In this study, multiple parameters are extracted from the mercury injection capillary pressure (MICP) curves and the degree of multicollinearity between these parameters is analyzed. The partial least squares regression (PLSR) method is used for establishing the permeability prediction model and the optimal number of latent variables of the model is determined by the leave-one-out cross-validation (LOOCV) method. A comparison of the existing empirical models, the permeability prediction model by ordinary least square (OLS) method, and the permeability prediction model by PLSR method based on the MICP curves indicates that the permeability prediction model by PLSR method is superior to the other models for tight sandstone reservoirs.