Intelligent sequence stratigraphy through a wavelet-based decomposition of well log data

Identification of sequence boundaries is an important task in geological characterization of gas reservoirs. In this study, a continuous wavelet transform (CWT) approach is applied to decompose gamma ray and porosity logs into a set of wavelet coefficients at varying scales. A discrete wavelet transform (DWT) is utilized to decompose well logs into smaller frequency bandwidths called Approximations (A) and Details (D). The methodology is illustrated by using a case study from the Ilam and upper Sarvak formations in the Dezful embayment, southwestern Iran. Different graphical visualization techniques of the continuous wavelet transform results allowed a better understanding of the main sequence boundaries. Using the DWT, maximum flooding surface was successfully recognised from both highest frequency and low frequency contents of signals. There is a sharp peak in all A&D corresponding to the maximum flooding surface (MFS), which can specifically be seen in fifth Approximation (a5), fifth Detail (d5), fourth Detail (d4) and third Detail (d3) coefficients. Sequence boundaries were best recognised from the low frequency contents of signals, especially the fifth Approximation (a5). Normally, the troughs of the fifth Approximation correspond to sequence boundaries where higher porosities developed in the Ilam and upper Sarvak carbonate rocks. Through hybridizing both CWT and DWT coefficient a more effective discrimination of sequence boundaries was achieved. The results of this study show that wavelet transform is a successful, fast and easy approach for identification of the main sequence boundaries from well log data. There is a good agreement between core derived system tracts and those derived from decomposition of well logs by using the wavelet transform approach

Estimation of vitrinite reflectance from well log data

Vitrinite reflectance (VR) data provide important information for thermal maturity assessment and source rock evaluation. The current study introduces a practical method for vitrinite reflectance determination from sonic and resistivity logs. The main determinant factor of the method is ΔRRS which is defined as the separation between cumulative frequency values of resistivity ratio (RR) and sonic log data. The values of ΔRRS range from −1 at ground level to +1 at bottom hole. The crossing point depth of the DT and RR cumulative frequency curves, where ΔRRS=0, indicates the onset of oil generation window. From the surface (ground level) to the crossing point depth ΔRRS takes negative values indicating organic material diagenesis window. Below the crossing point depth ΔRRS turns into positive values showing thermally-mature organic matter within the catagenesis window. Vitrinite reflectance measurements revealed strong exponential relationships with the calculated ΔRRS data. Accordingly, a new calibration chart was proposed for VR estimation based on ΔRRS data. Finally, an equation is derived for vitrinite reflectance estimation from ΔRRS and geothermal gradient. The proposed equation works well in the event of having limited VR calibration data

A Review of Reservoir Rock Typing Methods in Carbonate Reservoirs: Relation between Geological, Seismic, and Reservoir Rock Types

Carbonate reservoirs rock typing plays a pivotal role in the construction of reservoir static models and volumetric calculations. The procedure for rock type determination starts with the determination of depositional and diagenetic rock types through petrographic studies of the thin sections prepared from core plugs and cuttings. In the second step of rock typing study, electrofacies are determined based on the classification of well log responses using an appropriate clustering algorithm. The well logs used for electrofacies determination include porosity logs (NPHI, DT, and RHOB), lithodensity log (PEF), and gamma ray log. The third step deals with flow unit determination and pore size distribution analysis. To this end, flow zone indicator (FZI) is calculated from available core analysis data. Through the application of appropriate cutoffs to FZI values, reservoir rock types are classified for the studying interval. In the last step, representative capillary pressure and relative permeability curves are assigned to the reservoir rock types (RRT) based upon a detailed analysis of available laboratory data. Through the analysis of drill stem test (DST) and GDT (gas down to) and ODT (oil down to) data, necessary adjustments are made on the generated PC curves so that they are representative of reservoir conditions. Via the estimation of permeability by using a suitable method, RRT log is generated throughout the logged interval. Finally, by making a link between RRT’s and an appropriate set of seismic attributes, a cube of reservoir rock types is generated in time or depth domain. The current paper reviews different reservoir rock typing approaches from geology to seismic and dynamic and proposes an integrated rock typing workflow for worldwide carbonate reservoirs

Analyzing organic richness of source rocks from well log data by using SVM and ANN classifiers: A case study from the Kazhdumi formation, the Persian Gulf basin, offshore Iran

Determination of TOC is critical to the evaluation of every source rock unit. Methods which are dependent upon extensive laboratory testing are limited by the availability and integrity of the rock samples. Prediction of TOC (Total Organic Carbon) from well Log data being available for the majority of wells being drilled provides rapid evaluation of organic content, producing a continuous record while eliminating sampling issues. Therefore, the ideal method for determining the TOC fraction within source rock units would utilize common well log data. So a model was developed to formulate TOC values in the absence of laboratory TOC measurements from conventional well log data. Consequently, with the assistance of FL (Fuzzy Logic), TOC estimated from well log data with an overall prediction accuracy of 0.9425 for the test set. Following that TOC content of the Kazhdumi formation optimally has been divided into 4 zones using K-means cluster analysis, since searching for patterns is one of the main goals in data mining. There is a general increase in TOC from zone 1 to zone 4. The optimal number of zones has been detected by means of the knee method that finds the “knee” in a number of clusters vs. Compactness, Davies-Bouldin and Silhouette values. In the last step, using SVM (Support Vector Machine) and ANN (Artificial Neural Network) algorithms, two commonly used techniques, classification rules developed to predict the source rock class-membership (zones) from well log data. The proposed method is found effective in directly extracting patterns from well log data after defining classification rules. Quantitative comparisons of the results from ANN and SVM depicts that for classification problem of source rock zonation SVM with RBF (Radial Basis Function) kernel readily outperforms ANN in term of classification accuracy (0.9077 and 0.9369 for ANN and SVM, respectively), reduced computational time and highly repeatable results. This method would enable a more elaborate assessment of Kazhdumi formation to be undertaken by providing a comprehensive quick look results derived directly from well log data while using conventional methods one can’t define patterns within the data without grouping data manually

New Empirical Models for Estimating Permeability in One of Southern Iranian Carbonate Fields using NMR-Derived Features

Permeability is arguably the most important property in evaluating fluid flow in the reservoir. It is also one of the most difficult parameters to measure in field. One of the main techniques for determining permeability is the application of Nuclear Magnetic Resonance (NMR) logging across the borehole. However, available correlations in literature for estimating permeability from NMR data do not usually give acceptable accuracy in carbonate rocks. In this research, two new empirical models are introduced for quantifying NMR extracted permeability in carbonate formations. These models are validated for three carbonate formations, namely, Yamama, Gadvan, and Daryan in one of Iranian offshore reservoirs in the Persian Gulf. The first empirical model applies the pore-related NMR data such as free and bound fluid parameters. The second model, however, is a novel approach that uses the geometric features of the occurring humps in T2 distribution. For assessing the performance of the proposed models, statistical parameters as well as graphical tools are utilized. It is found that the for the examined case studies, geometric approach gives more accurate and reliable estimates compared to the available models in the literature including Timur-Coates and SDR methods

Simulation of NMR response from micro-CT images using artificial neural networks

The Nuclear Magnetic Resonance (NMR) log is amongst the functional techniques in petroleum investigation to segregating the reservoir and non-reservoir horizons precisely; furthermore, the NMR log provides an improved method to determine reservoir petrophysical parameters. Unfortunately, these data are usually sparse since acquiring NMR logs in producing cased wells is not possible and it is one of the most expensive tools in the logging industry thus its associated costs are the major limitation of its usage. Consequently, researchers have recently studied to virtually extract the NMR parameters via other routes. One such route, which we propose here is the possibility of estimating the T2 distribution curve and magnetization decay by establishing a relationship between micro-CT images and NMR parameters by means of artificial neural networks (ANN) and image analysis algorithms. Specifically, two ANN networks were designed, which numerically image features from micro-CT images as inputs, while the amplitude of the magnetization and relaxation time were output parameters. We assessed the procedure by taking the error rate and correlation coefficient into consideration and we conclude that the ANN model is capable of finding logical patterns between image features and NMR responses, and is thus able to reliably predict NMR response behavior. Furthermore, we quantitatively compared ANN and random walk (RW) NMR predictions, and we demonstrate that ANN readily outperforms RW in terms of accuracy

Full waveform acoustic data as an aid in reducing uncertainty of mud window design in the absence of leak-off test

Creating a mechanical earth model (MEM) during planning the well and real-time revision has proven to be extremely valuable to reach the total depth of well safely with least instability problems. One of the major components of MEM is determining horizontal stresses with reasonable accuracy. Leak-off and minifrac tests are commonly used for calibrating horizontal stresses. However, these tests are not performed in many oil and gas wellbores since the execution of such tests is expensive, time-consuming and may adversely impact the integrity of the wellbore. In this study, we presented a methodology to accurately estimate the magnitudes and directions of horizontal stresses without using any leak-off test data. In this methodology, full waveform acoustic data is acquired after drilling and utilized in order to calibrate maximum horizontal stress. The presented methodology was applied to develop an MEM in a wellbore with no leak-off test data. Processing of full waveform acoustic data resulted in three far-field shear moduli. Then based on the acoustoelastic effect maximum horizontal stress was calibrated. Moreover, maximum horizontal stress direction was detected using this methodology through the whole wellbore path. The application of this methodology resulted in constraining the MEM and increasing the accuracy of the calculated horizontal stresses, accordingly a more reliable safe mud weight window was predicted. This demonstrates that the presented methodology is a reliable approach to analyze wellbore stability in the absence of leak-off test

Seismic velocity deviation log: An effective method for evaluating spatial distribution of reservoir pore types

Velocity deviation log (VDL) is a synthetic log used to determine pore types in reservoir rocks based on a combination of the sonic log with neutron-density logs. The current study proposes a two step approach to create a map of porosity and pore types by integrating the results of petrographic studies, well logs and seismic data. In the first step, velocity deviation log was created from the combination of the sonic log with the neutron-density log. The results allowed identifying negative, zero and positive deviations based on the created synthetic velocity log. Negative velocity deviations (below − 500 m/s) indicate connected or interconnected pores and fractures, while positive deviations (above + 500 m/s) are related to isolated pores. Zero deviations in the range of [− 500 m/s, + 500 m/s] are in good agreement with intercrystalline and microporosities. The results of petrographic studies were used to validate the main pore type derived from velocity deviation log. In the next step, velocity deviation log was estimated from seismic data by using a probabilistic neural network model. For this purpose, the inverted acoustic impedance along with the amplitude based seismic attributes were formulated to VDL. The methodology is illustrated by performing a case study from the Hendijan oilfield, northwestern Persian Gulf. The results of this study show that integration of petrographic, well logs and seismic attributes is an instrumental way for understanding the spatial distribution of main reservoir pore types

Prediction of thermal maturity by indirect methods using seismic attributes in the central part of the Persian Gulf

AbstractIn this paper, a method is proposed for the prediction of thermal maturity in the source rock using indirect methods. The applied data are well logs (neutron, density, resistance, and acoustic) in 13 wells and seismic data in six oil and gas fields in the central part of the Persian Gulf. Well-logs and seismic data are much more abundant than geochemical data and cover an extensive area in the oil and gas fields. These properties compensate for the lack of geochemical data that are scattered and limited to a few wells. This study is carried out in two steps. First, the amount of thermal maturity in the Kazhdumi Formation is calculated from well logs and is presented as an index in each well. Data obtained from organic thermal evaluation analyses are used to validate the results of thermal maturity prediction. These data include Rock-Eval pyrolysis in two wells. Then, seismic data are processed and studied in two-dimensional sections at the location of the target fields. In this step, seismic attributes are extracted from the seismic data using the multi-attribute regression analysis method, and thermal maturity is calculated using these attributes. Prediction is performed by probabilistic neural network analysis, and a seismic section is extracted indicating variations in thermal maturity in the Kazhdumi Formation.Keywords: Source rock, Thermal maturity, Well log, Seismic attribute  Introduction The Kazhdumi Formation is an important source rock in the Persian Gulf basin, in the south of Iran (Bordenave Burwood, 1990). Thermal maturity of the Kazhdumi Formation is low in the central Persian Gulf compared to the eastern and western sectors (Rabbani 2008; Ghasemi-Nejad et al. 2009; Rezaie Kavanrudi et al. 2015; Rabbani et al. 2014; Baniasad et al. 2019). This formation is over-mature in proximity to the Hormoz Strait. The TOC content of this formation depends on the variety of depositional environments across the basin, increasing to the northwest with a maximum of 6 wt% in the proximity of the Hormoz strait (Rezaie Kavanrudi et al. 2015; Noori et al. 2016). The kerogen type is mainly ⅡS and type Ⅲ in different areas (Ghasemi-Nejad et al. 2009). The stratigraphic equivalents of the Kazhdumi Formation are the Burgan Formation in the west and south of the Persian Gulf (Kuwait), producing hydrocarbons from the second-largest hydrocarbon oilfield in the world, and the Nahr-Umr Formation in Qatar and Iraq. The Burgan and Nahr-Umr formations consist of fluvial sandstone in the south and east of the Persian Gulf compared to the shale-dominated volume in the north and central part (Ghasemi-Nejad et al. 2009; Noori et al. 2016).The source rock potential of this formation is largely unknown and there is a lack of published reports in the central part of the Persian Gulf. the aim of this study is to use log and seismic data for indirect estimation of thermal maturity in this area. Materials & MethodsIn this study, neutron, density and sonic as well as gamma-ray logs are used to predict thermal maturity. The maturity index (MI) by Zhao et al. (2007) was used to describe the level of thermal maturity based on well logs. The response of the neutron and density logs is affected by the fraction of water in a formation. The hydrocarbon density also decreases due to thermal maturation. The maturity index is calculated based on the equation from Zhao et al. (2007)(Eq. 1)             N: the number of log readings or the number of samples.Øn9i: neutron porosity of rock samples with 9% density porosity or higher. The 9% is a cut-off for porosity in the calculations. Values lower than 9% are indicative of very dense formations such as anhydrite or dense dolomite known as non-source shales. The cumulative value of the calculated MI is presented as a maturity index in the Kazhdumi Formation in each well. In the second step, seismic data analysis, inversion of seismic data and log prediction are carried out. Inversion analysis starts with seismic data processing using well logs and 2D post-stack seismic data. An acoustic impedance log is created by the combination of density and sonic logs. Check-shot data are used for depth-to-time conversion resulting to the correlation of well and seismic data.  Following this, an initial strata or impedance model is constructed. This model is produced using the seismic volume, available well data and defined horizons. Seismic data, the initial strata model as well as available wells are applied as the input data for inversion analysis, and then the inversion method is selected. Discussion of Results & ConclusionsGenerally, thermal maturity status in the Kazhdumi Formation is between immature to mature in the study area in the central part of the Persian Gulf. According to the presented data in this study, this formation is mainly immature in 2, 3, 5 and 7 fields. Field number 4 is early mature and number 1 is mature. Field 6 shows an immature to mature level indicating that the maturity varies in this field.In the next step, seismic attributes are selected by regression analysis for MI prediction. Attributes selection is a process for the extraction of seismic attributes from the raw seismic data for modeling the target log. Multi-attribute analysis is an automatic procedure for the selection of the most relevant seismic attributes to the target log.Finally, extracted attributes are used for the log prediction by Probabilistic Neural Network analysis (PNN). It is trained based on the selected attributes in the previous step and then predicts the target parameters.Acoustic impedance is recognized as the most important seismic attribute reflecting the geological properties (Chopra and Marfurt, 2005). The optimum number of attributes for MI prediction is four which shows the lowest prediction error, although the training error continuously decreases by adding more attributes. The validation plot of the target log is estimated by excluding the data step by step from the calculation. The reliability of the regression model is tested by comparing the prediction with the actual log values.Thereafter, the 2D seismic section is converted to an MI volume. The variation in MI is continuous laterally and vertically, therefore, can be tracked throughout the basin.A comparison of the computed MI with geochemical data indicated that this method is applicable for thermal maturity prediction in the study area. The increase in MI corresponds to an increase in the Tmax values, thus providing a good indicator of thermal maturity variation.The last point to consider is the significance of the selected seismic attributes and their relationship with the target parameter. Results indicated that acoustic impedance is the most important seismic attribute in MI prediction. Acoustic impedance contains information about the velocity and formation density which are both affected by the formation fluids (Broadhead et al. 2016; Atarita et al. 2017). It is inversely related to the organic matter content (Harris et al. 2019). The computed thermal maturity is inversely related to the neutron porosity and water saturation which are both controlling parameters of the acoustic impedance in a formation.Other attributes that have been used in the log prediction are time and amplitude-weighted frequency. These attributes are related to different geological properties of source/reservoir formations (Taner et al. 1994; Chen and Sidney 1997; Chopra and Marfurt 2005). Amplitude envelope (reflection strength) mainly represents acoustic impedance and is useful for identifying porosity, hydrocarbon and gas accumulation, sequence boundaries, and lithological/depositional environment variations (Chen and Sidney 1997; Hart 2002). Average frequency is defined as a signature of events and is useful for correlation, often reflecting oil and gas reservoirs by seismic attenuation (Taner et al. 1994). Amplitude-weighted frequency is a product of the amplitude envelope and the instantaneous frequency, providing a smooth estimation of instantaneous frequency by removing spikes and noises (Chen and Sidney 1997).To sum up, well logs and seismic attributes are successfully applied to predict thermal maturity in the Kazhdumi Formation in the central part of the Persian Gulf.The analysis shows that the Kazhdumi Formation is mainly immature to early mature in the central part of the Persian Gulf.Seismic data are spatially continuous which is an advantage in source rock evaluation, resulting in continuous predictions of thermal maturity in hydrocarbon fields.Using seismic data is also cost-effective and less time-consuming than geochemical testing