Well-log Interpretation Research Articles

Sequential binary classification of lithofacies from well-log data and their uncertainty quantification

Understanding and identifying the composition of various lithofacies in the subsurface is essential for successful reservoir characterization in hydrocarbon exploration. However, conventional methods such as core sampling and manual well-log interpretation are labor-intensive. As a result, many scientists are conducting research to use machine learning to study lithofacies more effectively and efficiently. However, as researchers are becoming more dependent on machine learning, an uncertainty analysis of machine-learning models is crucial to determine the reliability of the prediction results. Machine-learning algorithms that use ensemble methods provide an easy method for an uncertainty analysis but algorithms that do not use ensemble methods have difficulty in quantifying the level of uncertainty. In our research, we introduce a method known as sequential binary classification (SBC), which helps not only classify lithofacies but also to quantify and visualize the regions of uncertainty of the machine-learning models. SBC provides a method for using any classification algorithm of the user’s choice to construct an ensemble, which allows the user to quantify uncertainty readily. Our research uses the SBC algorithm to classify and quantify the uncertainty from the well-log data obtained from the North Sea near Norway. The results show that most of the lithofacies that exist in the region of interest share similar characteristics, which results in high uncertainty among the various lithofacies, and SBC helps to visualize these high uncertainties. We additionally demonstrate how SBC alleviates the class imbalance issue among the various lithofacies in the area, which is a very common problem in well-log data analytics.

Sedimentological and diagenetic facies of tight sandstones in lacustrine delta-front: A case study of the Jurassic Lianggaoshan Formation, eastern Sichuan Basin

In this study, taking the Jurassic Lianggaoshan Formation (J1l) tight sandstones in the eastern Sichuan Basin as an example, the types and well-logging responses of main sedimentological and diagenetic facies in the lacustrine delta-front are investigated based on summarizing the sedimentary characteristics and reservoir properties. Subsequently, further validation and application are conducted in the study area through machine learning. Research results show that the J1l lacustrine delta-front in the eastern Sichuan Basin mainly develops subaqueous distributary channels and mouth bar sand bodies, exhibiting typical densification reservoirs, with porosity and permeability distributed between 0.48% and 11.24% (av. 3.87%) and 0.0003–0.653 × 10−3 μm2 (av. 0.026 × 10−3 μm2), respectively. Strong compaction and strong cementation are the primary factors leading to densification, whereas chlorite coatings and weak dissolution play constructive roles in preserving some primary pores, creating a small amount of dissolution pores, and enhancing permeability. In terms of manifestation, the pore-throat content with a radius greater than 0.006 μm governs the reservoir quality. Furthermore, five types of diagenetic facies are identified in the J1l subaqueous distributary channels and mouth bars: strong compaction facies (Type I), strong cementation facies (Type II), chlorite-coating and intergranular pore facies (Type III), weak dissolution and intragranular pore facies (Type IV), and medium compaction and cementation facies (Type V). Overall, the thick and coarse-grained subaqueous distributary channels can be considered as the preferred exploration targets for tight oil and gas, with type III and type IV diagenetic facies being the most favorable reservoirs, characterized by well-logging responses of high AC and low GR, DEN, and RT. Based on the fine division of sedimentological and diagenetic facies, establishing well-logging interpretation models and then employing machine learning to achieve sweet spot reservoir prediction can provide valuable insights for tight oil and gas exploration in regions lacking core data.

The influence of salt tectonics on the distribution of the Triassic Skagerrak Formation in the Ula Field, Norwegian North Sea

Distribution of the Triassic succession in the North Sea is poorly understood because of structural complexities associated with halokinesis and limited stratigraphic control. This study uses a seismic and well-based dataset to improve understanding of development of the Triassic succession in the Ula Field Area, in the Norwegian North Sea. Core interpretation revealed a fluvial-dominated depositional environment in the Ula Field Area. Palynological studies allowed dating of cored intervals, revealing Ladinian and Carnian sections, time-equivalent to the Julius and Joanne members of the Skagerrak Formation. Well-log interpretation provided insight into the intra-Triassic stratigraphy of the Ula Field Area. A section considered to be equivalent to and extending from the Smith Bank Formation to the Jonathan Member of the Skagerrak Formation was interpreted and correlated across the area. In the proposed correlation, the Julius Member thins towards the Ula Field Area and is replaced by a time equivalent sandstone unit. The Jonathan Member displays a sandier composition compared to the equivalent section in the UK sector. Seismic facies-based interpretation of Triassic stratigraphy within salt minibasins allowed recognition and mapping of intra-Triassic units and showed that mudstone members thin towards the NE. Interpreted internal geometries within minibasins allowed determination of the timing of halokinesis. Integration of different datasets allowed palaeogeographic reconstructions for the Anisian, early Ladinian, Carnian and Norian to be constructed. To conclude, the distribution of stacked fluvial channel deposits indicates that they occur both within minibasins and across salt highs such that ongoing halokinesis had no topographic expression and that channels were free to migrate across the area.

Post-stack seismic inversion through probabilistic neural networks and deep forward neural networks

Seismic inversion, a nonlinear, non-unique-solution, and human-prone error problem, seeks the optimal solution in multivariate space, a task that AI algorithms perform better. So, inversion with AI becomes appropriate to extract information from post-stack data. A probabilistic neural network (PNN) and deep feedforward neural network (DFNN) inverted seismic data using multiattributes, and their results were compared with those provided by a model-based inversion (MBI). Applied to a post-stack volume of the Frontier Formation at the Teapot-Dome field, Wyoming-USA, the three methods provided porosity volumes (ϕ), gamma rays (GR), and Lamé impedances (µρ, λρ). The comparison between well-logs and the provided volumes revealed a reliable PNN prediction, while DFNN achieved the best geologically correlated parameter sections and a less reliable MBI prediction. The geomorphological time-slice analysis discriminated high ϕ-µρ areas and low GR–λρ areas, geologically interpreted as a deltaic environment, with distributary channels and beachfront lobes subject to the action of the tides, which agrees with the well-log interpretation and the basin geological evolution. The results enhanced the stacked section’s importance by the geological information contained, which would contribute to reducing the prospecting risk. Finally, PNN and DFNN encouraging results reaffirm their best searching-in-multivariate space ability.

Re-evaluation of the Hydrocarbon Potential of the Scarborough Field, Offshore Northern Carnarvon Basin, Australia.

This study examines the detailed basin analysis and re-evaluation of the hydrocarbon potential of the Scarborough Field in the Northern Carnarvon Basin, offshore Australia, aiming to enhance energy reserves. The research integrates extensive datasets, including a 3-D seismic reflection volume, fifty-two 2-D seismic lines, well logs from six wells, checkshot surveys, and surface geologic maps. The Northern Carnarvon Basin, a significant hydrocarbon reserve in Australia, has experienced a multi-phase extensional history and exhibits a complex stratigraphy linked to six tectonic phases. The pressing need for increased energy reserves necessitates revisiting old hydrocarbon fields to boost their production capacity using integrated methods. Well-log interpretation and correlation have revealed the lateral extents of petroleum system elements. The stratigraphic analysis identified three sequences and associated system tracts using the Depositional Sequence Model IV. Structural framework interpretation highlighted faults as migration pathways and hydrocarbon traps. Time and depth structure maps were produced to identify leads and prospects. Four leads and three prospects were pinpointed using RMS amplitude surfaces. Porosity values range from 20% to 29%, with hydrocarbon saturation between 51% and 65%. Volumetric assessment indicates that NAU Deep-1 contains 10.6 bcf, NAU Deep-2 holds 4.24 tcf, and NAU Deep-3 has 6.22 tcf of gas. These significant gas volumes highlight potential for future exploration and development in the Scarborough sedimentary infill.

Python Dash for Well Data Validation, Visualization, and Processing

Open-source Python libraries play a critical role in facilitating the digital transformation of the energy industry by enabling quick deployment of intelligent data-driven solutions. In this paper, we demonstrate an example of using Dash, a Python framework introduced by Plotly for creating interactive web applications. A fit-for-purpose software was tailored for an in-house research project in well-data validation, visualization, and processing. The application automates quality control of different sets of well-log data files (DLIS/LIS or LAS) for completeness, validity, and repeatability. For this tedious and critical process, a human expert is required to perform tasks using well-log interpretation software. A typical digital log file may contain hundreds or thousands of data channels that are difficult are difficult to visualize and validate manually. Sometimes it takes multiple iterations of communication between the data provider and the data receiver to achieve a final valid deliverable copy. By utilizing open-source Python libraries, such as DLISIO (Equinor ASA, 2022) and LASIO (Inverarity, 2023), a web interface based on Plotly-Dash is developed to visualize and check all data channels automatically and then produce a compliance summary report in PDF or HTML format. The time for validating one DLIS file that has hundreds of data channels is significantly reduced. Implementation of this automated data quality control workflow demonstrates that open-source Python libraries can significantly reduce the time from development to the deployment cycle. Quick implementation of intelligent software based on Python Plotly-Dash enables customized solutions or workflows that further improve both the effectiveness and efficiency of routine data quality control processes.

Machine learning approaches for formation matrix volume prediction from well logs: Insights and lessons learned

Determining the formation rock type and their petrophysical properties using well-log data is necessary for resource assessment. Formation porosity, shale content, and saturations must be estimated to quantify hydrocarbon reserves. Interpreting well logs could be challenging, especially when the formation is highly heterogeneous. Conventional physics-based interpretation can be tedious and time-consuming. Machine learning (ML) models can automate the well-log interpretation process, saving time and reducing the need for the expertise of trained engineers. In this research, we utilized five supervised ML algorithms incorporating multiple well-log parameters to identify patterns and behaviours in the well-log dataset. Our objective is to provide accurate, real-time predictions of sandstone, carbonate, and shale mineral volumes in the formation. We used the data from open-hole well logs of four wells in the Volve field and trained ML models using Random Forest, Gradient Boosting, Support Vector Regression, K-Nearest Neighbour, and Artificial Neural Networks. We used a robust framework that included random and grid search and cross-validation techniques to find the best hyperparameters and minimize bias and variance in the ML models. ML models were built by training and testing data from two wells and then validated on unseen data from the next two wells.All the ML models were able to predict mineral volume both on testing data as well as on the validation set. K-Nearest Neighbour, Random Forest, and Gradient Boosting outperform the other ML models. The overall prediction accuracy is similar for these three models, and the Random Forest model can be taken as the best model based on Taylor's plot. Random Forest can reduce the variance of the model to minimize the overfitting of decision trees. Gradient boosting builds trees sequentially and focuses on weak predictors to minimize a loss function and achieve better accuracy. We observed that all predictive ML models could easily quantify shale content but have difficulties differentiating sandstone and carbonate. The KNN model results are better than the other ML models for predicting carbonate volumes. One challenge when using ML techniques in the current state is that the accuracy of the model is limited to wells with similar drilling conditions as the training data, without significant changes in geological conditions. This means that a domain expert is needed to assist with predictive modelling using machine learning algorithms. Despite this challenge, our results show that using a machine-learning framework for reservoir characterization can be effective. However, ML models should not be viewed as a replacement for traditional interpretation methods but rather as an additional tool that can be used by engineers involved in petrophysical evaluations.

Improving multiwell petrophysical interpretation from well logs via machine learning and statistical models

Well-log interpretation estimates in situ rock properties along well trajectory, such as porosity, water saturation, and permeability, to support reserve-volume estimation, production forecasts, and decision making in reservoir development. However, due to measurement errors, variability of well logs caused by multiple measurement vendors, different borehole tools, and nonuniform drilling/borehole conditions, estimations of rock properties with original well logs without proper preprocessing may not be accurate, especially in the context of multiwell estimation. Well-log normalization techniques such as two-point scaling and mean-variance normalization are commonly used to improve the robustness of multiwell rock-property estimation. However, these techniques do not consider the correlation between well logs and require subjective knowledge for their effective implementation. To reduce uncertainties and processing time associated with multiwell rock-property estimation from well logs, we develop discriminative adversarial (DA) and linear constraint models for well-log normalization and rock-property estimation. The DA neural network model developed for well-log normalization and interpretation can perform linear and nonlinear well-log normalization while considering the joint distribution of each well log and rock properties. However, the linear constraint model uses an ensemble of predictions from linear models to constrain well-log normalization and rock-property estimation. We also develop a divergence-based type well identification method to select type (training) wells for a test well based on the statistical similarity of associated well-log distributions instead of the interwell distance. We apply the DA model to perform well-log normalization and prediction of permeability for the Seminole San Andres Unit carbonate reservoir. Compared with the permeability predicted with the classical machine learning model without well-log normalization and models with two-point scaling normalization, the DA model yields the most accurate permeability prediction by decreasing the mean-squared error of permeability prediction by 20%–50%.

Automated Well-Log Pattern Alignment and Depth-Matching Techniques: An Empirical Review and Recommendations

Well logging has been an integral part of decision making at different stages (drilling, completion, production, abandonment) of a well’s history. However, the traditional human-reliant approach to well-log interpretation, which has been the most common practice in the industry, can be time consuming, subjective, and incapable of identifying fine details in log curves. Previous studies have recommended automated approaches as a candidate for addressing these challenges. Despite the progress made so far, what is not yet clear from the existing literature is the extent to which these automated approaches can dispense with human interventions in real-life scenarios. This paper presents an empirical review of different depth-matching techniques in real-life timelapse well logs, primarily focusing on gamma ray and the extent to which the outcomes of these techniques match the results from a human expert. Specifically, the performances of dynamic time warping (DTW), constrained DTW (CDTW), and correlation optimized warping (COW) are investigated. The experiments also consider the effects of filtering and normalization on the performance of each of the techniques. Concerning the correlations of each technique’s outcome with the reference data and an expert-generated outcome, this research identifies and discusses its key challenges, as well as provides recommendations for future research directions. Although the COW technique has its limitations, as discussed in this paper, our experiments demonstrate that it shows more potential than DTW and its variants in the well-log pattern alignment task. The work entailed by this research is significant because identifying and discussing the limitations of these techniques is vital for solution-oriented future research in this area.

Deep-learning missing well-log prediction via long short-term memory network with attention-period mechanism

Underground reservoir information can be obtained through well-log interpretation. However, some logs might be missing due to various reasons, such as instrument failure. A deep-learning-based method that combines a convolutional layer and a long short-term memory (LSTM) layer is proposed to estimate the missing logs without the expensive relogging. The convolutional layer is used to extract the depth-series features initially, which are then input into the LSTM layer. To improve the feature memory and extraction capabilities of the LSTM layer, we construct two LSTM-based components: the first component uses an attention mechanism to optimize the LSTM units by adaptively adjusting network weights, and the second component uses a period-skip mechanism, which enhances the sensitivity of aperiodic changes in the depth series by learning the information of the shallow sequence. In addition, we add an autoregressive component to enhance the linear feature extraction capability while learning the nonlinear relationship between different logs. A total of 13 wells from two different regions are used for experiments, including 11 training and two test wells. We use one well to calculate the uncertainties of four time-series networks, i.e., our proposed network and three benchmark models (recurrent neural network, gated recurrent unit, and LSTM), to demonstrate the stability and robustness of the proposed method. Furthermore, we evaluate the performance of our proposed method in several crossover experiments, e.g., different logging intervals, depths, and input logs. Compared to a state-of-the-art deep learning method and a classic LSTM network, the proposed network has higher reliability, which is reflected in the feature extraction of depth series with a larger span. The experimental results demonstrate that our proposed network can accurately generate sonic and other unknown logs.

Fluid identification based on geophysical well logs in ultralow-porosity tight sandstone reservoirs

Deep tight sandstone reservoirs typically exhibit ultralow porosity and permeability owing to their small pore size, narrow pore throats, and poor pore connectivity. In heterogeneous tight formations, well-log interpretation is uncertain, and it is difficult to identify fluid types using the classic picket-plot (resistivity versus porosity crossplot) method. To better identify and characterize gas-bearing zones in heterogeneous tight sandstone reservoirs, effective sensitivity factors for fluids need to be constructed. From the conventional logging principle, the acoustic neutron porosity difference, density-neutron porosity difference, and triple-porosity ratio are all sensitive parameters for gas-bearing layers. In addition, the density and nuclear magnetic resonance (NMR) porosity difference and [Formula: see text] geometric mean of the movable fluid are sensitive to gas saturation. Based on these parameters, a series of fluid typing crossplots was constructed, and their effectiveness was compared. In contrast, the interpretation results from NMR logging are better, and the dual-porosity difference and triple-porosity ratio methods from conventional logs also are effective when NMR logging is not available. In practice, these methods are used to provide their results, and then they are combined using a voting strategy according to the performance of each crossplot. In two case studies of the Kuqa Depression of the Tarim Foreland Basin, China, the new fluid typing method proved effective, and it provides an alternative method for fluid identification using nonelectrical logs (when electrical logs are not available or applicable).

Pore-scale modelling of elastic properties in hydrate-bearing sediments using 4-D synchrotron radiation imaging

Gas hydrate contains abundant methane gas, which is considered to be a promising energy resource to mitigate the influence of climate change in the future. Understanding the effect of fluids-solid-hydrate spatial distribution on elastic properties of hydrate-bearing sediments benefits the well-log interpretation. We constructed 4-D hydrate-bearing models covering a wide hydrate saturation range based on high-resolution digital images obtained by Synchrotron Radiation X-Rays Computed Tomography (SRXCT). We analysed the elastic moduli and elastic wave velocities of these models by the static Finite Element Method (FEM). The results demonstrated that the dominant hydrate morphology transits from pore-filling to load-bearing when hydrate saturation increases. At low hydrate saturations, the hydrate formed around some gas bubbles blocks local pore space. Therefore a great enhancement in compressional wave velocity was observed at low hydrate saturations. While the shear wave velocity was less affected due to the presence of thin water films. Based on the simulated results, an empirical model was developed to compute the compressional and shear wave velocities considering the free gas phase. The comparison with some experimental measurements validated the applicability of the model.

Sensitivity and Uncertainty Analysis for Natural Gas Hydrate Production Tests in Alaska

The Japan Research and Development Consortium for Pore Filling Hydrate in Sand (MH21-S), the U.S. Department of Energy National Energy Technology Laboratory (DOE NETL), and the U.S. Geological Survey (USGS) are planning a long-term production test from gas hydrate (methane hydrate) bearing reservoirs in the Prudhoe Bay Unit in northern Alaska. Prediction of gas and water production using 2D reservoir simulation models can be very useful for decision-making for long-term production testing. However, the input parameters applied to the simulation model, which are essentially based on the most likely values obtained from the well-log interpretation and core sample measurements of the Hydrate-01 stratigraphic test well (STW), have some degrees of uncertainty. In this study, to evaluate the impact of uncertainties in the input model on the prediction results of gas and water production as well as to analyze how each input parameter contributes to the uncertainties of prediction results, we conducted uncertainty analysis and parameter sensitivity analysis based on two different methods (the regression-based method and the Sobol’ method). The sensitivity analyses reveal that the parameters regarding relative permeability (exponential of the gas relative permeability (Ng) and residual gas saturation (Sgcr)) and the intrinsic/effective permeability of the lower B1 sand significantly contribute to the uncertainties of the gas and water production predictions. In addition, the uncertainty analyses reveal that the P50 value of cumulative gas production can result in less than the reference case with the most likely values for the input parameters due to the uncertainty of Ng. These results indicate that reducing the uncertainties of gas relative permeability and residual gas saturation is essential for more reliable numerical simulations both for prediction of test well performance and subsequent history matching of field test results.

Machine learning applications for well-logging interpretation of the Vikulov Formation

Nowadays well logging curves are interpreted by geologists who preprocess the data and normalize the curves for this purpose. The preparation process can take a long time, especially when hundreds and thousands of wells are involved. This paper explores the applicability of Machine Learning methods to geology tasks, in particular the problem of lithology interpretation using well-logs, and also reveals the issue of the quality of such predictions in comparison with the interpretation of specialists. The authors of the article deployed three groups of Machine Learning algorithms: Random Forests, Gradient Boosting and Neural Networks, and also developed its own metric that takes into account the geological features of the study area and statistical proximity of lithotypes based on log curves values.

Ensemble-based well-log interpretation and uncertainty quantification for well geosteering

Hydrocarbon reservoirs are often located in spatially complex and uncertain geologic environments, where the associated costs of drilling wells for exploration and development are notoriously high. These costs may be reduced with an optimized well-placement strategy based on real-time geologic information, known as well geosteering. To effectively place the well in an updated geomodel and support well geosteering decisions in real time, we apply an iterative inversion approach based on the Levenberg-Marquardt form of the ensemble randomized maximum likelihood method. The method estimates geomodel properties together with their uncertainties by reducing the statistical misfit between the measurements acquired with well-logging tools and the predicted measurements from numerical simulations. Analyses of synthetic cases indicate that the method’s reliability and computational speed depend on the distance from the logging tool to formation boundaries, the contrast of model properties, and the thickness of formation layers. Our method delivers reliable estimates of model properties with only 40 ensemble members and 2–10 iterations; hence, it is approximately 10–125 times faster than Metropolis-Hastings Monte Carlo, which we use as a baseline condition given its proven track record. Likewise, our method is amenable to parallelization to further reduce computational times. Implementation of the method with a synthetic example inspired by a historical well geosteering operation yields accurate formation evaluation and verifies its accurate and reliable performance under complex geologic conditions.

Evaluation of Petrophysical Characteristics of Mishrif and Yamama Reservoirs, in Garraf Oil Field, Southern Iraq, Based on Well-Logging Interpretation

The Mishrif and Yamama Formation are the main reservoirs in the Garraf oilfield, Nasiriyah city, of the Euphrates Subzone, Mesopotamian Basin, Southern Iraq. The Garraf oilfield structural pattern corresponds with the similar anticline forming the oilfields of Rafidain and Dujaila and is parallel to the main trend of Zagros, indicating simple coaxial deformation. The petrophysical properties are evaluated using the Interactive Petrophysics V3.5 software. This comprises determining the lithology, mineralogy, and matrix for the investigated reservoirs, as estimating clay volume, total, effective, and secondary porosity, water saturation, permeability, and determining the net pay and gross thickness. The findings of this investigation indicated that the Mishrif formation is divided into two main units separated by a marl layer, with the upper unit having poor reservoir qualities and the lower unit having favorable ones. As a result, there are nine reservoir units in the lower main unit (M1, M1.2, M2, L1, L1.2, L2, L2.2, L2.3, and L2.4). The best and largest reservoir unit capacity is Unit L1.2, with exceptional petrophysical characteristics. Lower units L2.2, L2.3, and L2.4 are nearly saturated in reservoir water, with a little oil in some wells. YA, YB1, and YB2 are the three reservoir units that make up the Yamama formation. Unit YA is the best reservoir unit because of its petro physical properties.

Multiscale petrophysical characterization and flow unit classification of the Minnelusa eolian sandstones

Integration of petrophysical and geological information is critical to simulation of subsurface carbon storage (GCS). In this sense, two depositional facies were identified from the core description and well-log interpretation, namely massive (MS) and cross-bedded (CB) facies groups. Additionally, pore-scale characteristics were studied by a combination of techniques, e.g. Nuclear Magnetic Resonance (NMR) and mercury intrusion capillary pressure (MICP). Scanning electron microscope (SEM) and petrographic analyses show that the pore structure is dominantly controlled by the depositional environment and dolomite cementation. NMR-T2 distributions of MS and CB facies show triple and quadruple modes, respectively. In addition, MICP of high- and low-permeability MS facies samples, and their CB facies group mixtures were collected. The MS sample pore-throat size distribution is uni-modal, while the triple-modal characteristic of the mixtures indicates heterogeneous pore structures at the sub-core scale for CB facies. The reliably estimates of porosity and permeability for both facies groups via NMR techniques and the MLR (Multiple Linear Regression) approach demonstrate the applicability of these techniques to eolian sandstone. Moreover, irreducible water saturation via the T2-cutoff method correlates strongly with T2LM instead of porosity. Finally, the rock quality index and flow zone indicator were calculated based on Combinable Magnetic Resonance (CMR) log interpretations. This provides direct connection to properties measured in the well. Four flow units were classified for both facies groups. Results show that better reservoir quality with significant heterogeneities is observed in the CB facies. This study highlights the importance integrating multiscale petrophysical properties including facies, pore architecture and diagenesis analysis with core- to log-scale property characterization. The results herein validate our reservoir characterization and flow unit classification in eolian reservoirs.

The salinity dependence of electrical conductivity and Archie's cementation exponent in shale formations

Electrical conductivity/resistivity is one of the key petrophysical parameters for well-log interpretation. However, in the shale formations, the fluid saturation determined from the well-known Archie-related equations is inaccurate. The commonly accepted reason is that clays within shale formations provide Cation Exchange Capacity (CEC) and additional surficial conduction pathways that are not properly accounted for in water saturation equations leading to the so-called “non-Archie” phenomenon. To investigate this, the Archie parameters were determined for 5 shale samples with CEC controlled by fully saturating different levels of pore water salinity. The electrical conductivity measurement underwent 2800 psi confining pressure to simulate reservoir conditions. The effective porosity was determined by comparing the sample weight in both dry and fully saturated states. Nuclear Magnetic Resonance (NMR) was used for the pore size distribution investigation. Only a small change in Archie's cementation exponent m was observed for different porewater salinities (0.1–0.15) indicating a minor contribution from CEC for shale samples, however, the change in pore water conductivity leads to altered electrical conduction pathways consistent with a volume averaging approach. In contradiction with commonly held belief, it is shown that the rock bulk conductivity and likewise Archie's cementation exponent, m, is not monotonically increasing with the pore water conductivity and therefore CEC has a minor effect on Archie's cementation exponent. This implies that confining pressure and therefore depth, is the main cause of the non-Archie phenomenon.

Petrophysical characteristics of Silurian and Ordovician shale gas formations in the Baltic Basin (Northern Poland)

This paper presents findings from the study into the relationships between the laboratory- and well log-derived data (including the comprehensive well-log interpretation data) on petrophysical properties of the Silurian and Ordovician shale gas formations in the Baltic Basin (North Poland). Approximately 70 samples of mudstone were examined in laboratory experiments to determine total and effective porosity; bulk, grain, and rock density; total organic carbon; physical permeability; total pore area, pore distribution; and mineral components. Some rock samples were further investigated using mercury injection porosimetry, helium porosimetry, dual liquid porosimetry, NMR, N2 adsorption/desorption, rock-eval pyrolysis, and XRD to obtain the targeted petrophysical information from heterogeneous claystone/mudstone and their organic matter. Natural radioactivity, bulk density, total porosity, volume of kerogen, and other parameters were determined from the continuous curves plotted from the earlier depth matched well logging data, which were used to account for differences between vertical resolution of well logs and the point data obtained from the laboratory. The goal of this study was to provide a comprehensive characterization of the Silurian and Ordovician shale gas formations in Northern Poland to support the thesis about their heterogeneity.

The Pressure Dependence of the Archie Cementation Exponent for Samples from the Ordovician Goldwyer Shale Formation in Australia

Summary Applying the realistic cementation exponent (m) in Archie’s equation is critical for reliable fluid-saturation calculation from well logs in shale formations. In this study, the cementation exponent was determined under different confining pressures using a high-salinity brine to suppress the surface conductivity related to the cation-exchange capacity of clay particles. A total of five Ordovician shale samples from the Canning Basin, Australia, were used for this study. The shale samples are all illite-rich with up to 60% clay content. Resistivity and porosity measurements were performed under a series of confining pressures (from 500 to 8,500 psi). Nuclear magnetic resonance (NMR) was used to obtain porosity and pore-size distribution and to detect the presence of residual oil. The complex impedance of samples was determined at 1 kHz to verify the change in pore-size distribution using the POLARIS model (Revil and Florsch 2010). The variation of shale resistivity and the Archie exponent m at different pressures is caused by the closure of microfractures at 500 psi, the narrowing of mesopores/macropores between 500 and 3,500 psi, and the pore-throat reduction beyond 3,500 psi. This study indicates that unlike typical reservoirs, the Archie exponent m for shale is sensitive to depth of burial because of the soft nature of the shale pore system. An equation is developed to predict m under different pressures after microfracture closure. Our study provides recommended experimental procedures for the calculation of the Archie exponent m for shales, leading to improved accuracy for well-log interpretation within shale formations when using Archie-based equations.