Water Saturation Logs Research Articles

Enhanced history matching process by incorporation of saturation logs as model selection criteria

This paper proposes a methodology for an alternative history matching process enhanced by the incorporation of a simplified binary interpretation of reservoir saturation logs (RST) as objective function. Incorporating fluids saturation logs during the history matching phase unlocks the possibility to adjust or select models that better represent the near wellbore waterfront movement, which is particularly important for uncertainty mitigation during future well interference assessments in water driven reservoirs. For the purposes of this study, a semi-synthetic open-source reservoir model was used as base case to evaluate the proposed methodology. The reservoir model represents a water driven, highly heterogenous sandstone reservoir from Namorado field in Brazil. To effectively compare the proposed methodology against the conventional methods, a commercial reservoir simulator was used in combination with a state-of-the-art benchmarking workflow based on the Big Loop™ approach. A well-known group of binary metrics were evaluated to be used as the objective function, and the Matthew correlation coefficient (MCC) has been proved to offer the best results when using binary data from water saturation logs. History matching results obtained with the proposed methodology allowed the selection of a more reliable group of reservoir models, especially for cases with high heterogeneity. The methodology also offers additional information and understanding of sweep behaviour behind the well casing at specific production zones, thus revealing full model potential to define new wells and reservoir development opportunities.

Robust estimation of shear wave velocity in a carbonate oil reservoir from conventional well logging data using machine learning algorithms

In recent decades, various applications of shear wave velocity have been reported in oil and gas projects. These applications include the determination of lithology, estimation of geomechanical parameters, reservoir fluid detection, etc. The shear wave velocity is usually measured or recorded by DSI (Dipole Shear Sonic imager) instruments. This log is not usually available in all wells of a hydrocarbon field because of the high cost of running this log compared to other conventional logs. However, it is tried to estimate the shear wave velocity from other related logs.The literature contains many empirical rock physics models used to estimate shear wave velocity. Most of them were developed for sandstone lithologies and didn't applicable in carbonate reservoir because of existing some vertical and horizontally complex fracture networks in the carbonate reservoirs that cause a quite different behavior for the shear wave velocity.The main objective of this research is to concentrate on the effect of lithology in the estimation of the target log for a carbonate reservoir. At the earliest step, preprocessing is performed and 455 data points of several conventional well logs, i.g., acoustic, density, neutron porosity, resistivity, gamma-ray, calcite volume, dolomite volume, and water saturation logs have been applied to generate the synthetic shear wave velocity log in a carbonate oil reservoir.First, original data are divided into training, validation, and testing subsets. Different data-driven predictive models were built: Multiple Linear Regression (MLR), Ensemble Learning Method (ELM), and three architectures of Artificial Neural Networks (ANN). The performance of all techniques was compared with each other. Finally, the feed-forward neural network shows the highest accuracy with R-values of 0.99 and 0.96 for the training and testing datasets. Moreover, the network design and tuning parameters of the model are adjusted using the grid search optimization technique to obtain the optimal design of the artificial neural network for estimating the shear wave velocity log. Hence, a deep artificial neural network, i.e., [8–11–15-1] network with bayesian regularization training algorithm and hyperbolic tangent sigmoid transfer function, is proposed to predict the target response in other wells.

Pay Zone Determination Using Enhanced Workflow and Neural Network

Amplitude versus offset (AVO) analysis and attributes are frequently utilized during the early stages of exploration when no well has been drilled. However, there are still some drawbacks to this method, including the fact that it involves a substantial amount of time and experience, as well as the subjectivity of manual analysis. By utilizing unsupervised learning, this process can be done more objectively and faster. Unsupervised learning can detect anomalies and identify patterns to understand more about the datasets since, at this early stage of exploration, there is still a lack of information and labelling. A type of unsupervised learning referred to as self-organizing maps (SOM) is applied in this study to delineate hydrocarbons from given AVO properties that were used to detect hydrocarbons. SOM is also used to eliminate redundancy in the selection of attributes prior to the delineation procedure. The investigation began with well log data and progressed ahead into multiple fluid conditions to evaluate the model’s ability to identify hydrocarbons. The analysis can then be extended to the seismic dataset. By combining SOM, correlation coefficient, and mean–median, a method is devised for filtering features to remove redundancy. On the hydrocarbon delineation process, the model managed to detect hydrocarbons using well log simulations and was confirmed using water saturation logs. Additionally, the model is validated using real seismic data, demonstrating a promising performance in defining probable hydrocarbons. The proposed method enables early detection of hydrocarbon content during the preliminary stage of exploration when no well is accessible.

Elastic Property Modeling, Extended Elastic Impedance (EEI) and Curve-Pseudo Elastic Impedance (CPEI) Inversion for Pore Type Analysis and Hydrocarbon Distribution in Carbonate Reservoir, Kujung I Formation, “Humaira” Field, North East Java Basin

The complexity of the pore shape in carbonate rocks causes the need for a special strategy to characterize carbonate reservoir. The more information used, the more accurate the reservoir characterization will be. Pore type analysis is the important study because it relates to the fluid flow properties. The elastic property modeling show a good match to the actual data. The results of the well log and petrophysical data analysis show that the gas zone is located at the upper side of Kujung I Formation. Based on rock physics modeling result, the possible pore type developing in the Kujung I Formation is reference pore with the dominance of the aspect ratio value of about 0.17-0.19. The carbonate layer containing hydrocarbons is characterized by low Lamda-Rho, Lamda/Mu values and a low Poisson ratio. Porous carbonate layer, characterized by a low Mu-Rho value. The slice results show that the gaseous area is located on the anticline. The zone that has good porosity indicated by low Mu-Rho. In the IN-3 well there are no hydrocarbons, this analysis is in accordance with the geological condition of the IN-3 well which is in a low area on the time structure map. The inversion results show a good match between CPEI against water saturation log and CPEI against porosity log.

Water saturation calculation using fractional flow and production logging data in a Caspian region sandstone petroleum reservoir

Water saturation in porous media plays a significant role in static, dynamic reservoir modeling and petroleum reserves calculations. In this paper, a Pliocene conglomerate type of sandstone Caspian reservoir was studied. The objective of this paper is to apply water saturation calculation method using fractional flow and PLT (Production Logging Tool) and to compare the results with other methods. In this study, 6 petrophysical rock typing were defined by two methods at log level using the porosity, water saturation logs and RQI(Reservoir Quality Index). Core porosity and permeability correlations were calculated for each rock type where k-phi transforms were generated, respectively. Relative Permeability data of the reservoir was classified based on the rock types. Oil-Water relative permeability ratios versus water saturation data were correlated exponentially. A solution for water saturation was derived at log level using fractional flow, relative permeability, core porosity, permeability and production data. Leverett-J function and Archie saturations were calculated to compare to the proposed fractional flow approach. The comparison of the saturation logs with J-function and Archie saturation showed good match. This approach can be considered as a tool to validate the saturation-J function. This methodology helps to estimate pre-production water cut performance of the wells in reservoir models considering that not all the wells will start flowing at 100% dry oil. The employed methods regarding the petrophysical rock typing and water saturation calculations will facilitate reservoir characterization for future field development activities.

New AVO Attributes and Their Applications for Facies and Hydrocarbon Prediction: A Case Study from the Northern Malay Basin

Amplitude versus offset (AVO) analysis integration to well log analysis is considered one of the advanced techniques to improve the understanding of facies and fluid analysis. Generating AVO attributes are one solution to give an accurate result in facies and fluid characterization. This study is focused on a field of Northern Malay basin, which is associated with a fluvial-deltaic environment, where this system has high heterogeneity, whether it is vertically or horizontally. This research is aimed to demonstrate an application of the scale of quality factor of P-wave (SQp) and the scale of quality factor of S-wave (SQs) AVO attributes for facies and fluid types separation in field scale. These methods are supposed to be more sensitive to predict the hydrocarbons and give less ambiguity. SQp and SQs are the new AVO attributes, which derived from AVO analysis and created according to the intercept product (A) and gradient (B). These new attributes have also been compared to the common method, which is the Scaled Poisson’s Ratio attribute. By comparing with the Scaled Poisson’s Ratio attribute, SQp and SQs attributes are more accurate in determining facies and hydrocarbon. SQp and SQs AVO attributes are integrated with well log data and considered as the best technique to determine facies and fluid distribution. They are interpreted by using angle-stack seismic data based on amplitude contrast on interfaces. Well log data, e.g., density and sonic logs, are used to generate synthetic seismogram and well tie requirements. The volume of shale, volume of coal, porosity, and water saturation logs are used to identify facies and fluid in well log scale. This analysis includes AVO gradient analysis and AVO cross plot to identify the fluid class. Gassmann’s fluid substitution modeling is also generated in the well logs and AVO synthetics for in situ, pure brine, and pure gas cases. The application of the SQp and SQs attributes successfully interpreted facies and fluids distributions in the Northern Malay Basin.

Application of wavelet transform to detect fractured zones using conventional well logs data (case study: southwest of Iran)

Detection of fractured zones is an indisputable challenge in fractured reservoir characterisation. Although fracture identification from core data and image logs are direct and effective, they are costly and not commonly used. In this study, the discrete wavelet transform (DWT) was applied to decompose water saturation log data to detect fractured zones and calculate the number of fractures. Herein, the energy matching strategy and wavelet packet were used to select the optimum mother wavelet. The uranium log was applied as a filter to reduce errors which improved the accuracy of results for prediction of fractured zones by 90%. Finally, the relationship between signal energy of water saturation log and the number of fractures in each fractured zone was studied. The proposed method was successfully tested in wells in the Asmari formation (South Western Iran) and provides significant scope for application in other analogous field.

Fracture detection from water saturation log data using a Fourier–wavelet approach

Fracture detection as applied to reservoir characterization is a key step towards modeling of fractured reservoirs. While different methods have been proposed for detection and characterization of fractures and fractured zones, each is associated with certain shortcomings that prevent from their full use in different related engineering application environments. In this paper a new method is proposed for detection of fractured zones and fracture density in which water saturation log data is utilized. For detection of fractures, we have used wavelet transform and properties of wavelets that are highly suitable for detection of changes and local features of data. To choose the optimum mother wavelet, we have used energy matching strategy in which a wavelet with the highest energy match between spectral energy of the signal at the dominant frequency band and the coefficient energy at the same band of wavelet decomposition of the signal is selected. We have used wavelet packet for a more narrow frequency band selection and enhanced results. Decomposing the water saturation data using wavelets showed that the majority of information of the original log is hidden at low frequency bands. As a result, approximated section of wavelet transform of data was used for fracture detection, while shale volume (or gamma ray) log data was used to filter part of the errors in prediction and identification of the uncertain zones. This increased the accuracy of the results by 70%. Finally, a linear relation was derived between energy of approximated section of water saturation log and fracture density, allowing us to estimate the number of fractures in each fractured zone. The method was applied to four wells belonging to one of the Iranian oilfields located in the southwest region of the country and the results are promising. The use of large volume of data and the subsequent analysis increased the generalization ability of the proposed method.

Reservoir Aspects of Ekofisk Subsidence

In Nov. 1984, Phillips Petroleum Co. discovered subsidence of the seabed overlying the Ekofisk oil reservoirs offshore Norway. This phenomenon is the result of the compaction of the porous chalk reservoirs and the transmission of this compaction through the overburden to the seafloor. This paper describes the geolgoic- and reservoir-related aspects of subsidence, including the mechanism leading to reservoir compaction and its effect on reservoir performance. The compaction of the Ekofisk reservoirs is shown to be a result of pore-pressure depletion. Although some of the compaction is elastic, the bulk results from plastic deformation (pore collapse) of high-porosity chalk. Reservoir compaction is shown to cause subsidence of the seabed through deformation of the overlying sediments. Reservoir compaction has also had a pressure-maintenance effect on the reservoir. Thus far, no loss in reservoir productivity has been observed. On the basis of analogy with the neighboring West Ekofisk field, no loss in reservoir productivity is anticipated, at least for the near future.