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

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

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

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

An integrated approach to study the impact of fractures distribution on the Ilam-Sarvak carbonate reservoirs: a case study from the Strait of Hormuz, the Persian Gulf

Most of the Iranian hydrocarbon reservoirs in the Persian Gulf Basin and the Zagros Fold-Thrust Belt are composed of fractured carbonate rocks. In this regard, determining the spatial distribution of fractures has been a challenging issue. In this study, an integrated approach was applied for understanding the impact of fractures spatial distribution on the Ilam-Sarvak (Cenomanian to Santonian) carbonate reservoir rocks. For this purpose, seismic interpretation techniques along with geomechanical and geostatistical modeling were employed to characterize fractures at different scales. Initially, the relationship between fractures origin and the normal faults was investigated by conducting an in-situ stress analysis. Afterwards, the velocity deviation log (VDL) and fracture intensity log (FIL) were derived as fracture attributes from the interpretation of Formation Micro Imager (FMI) and conventional well logs. A 3D model of VDL and FIL was achieved by using a sequential Gaussian simulation (SGS) method. In order to achieve a more realistic and accurate model of the factures distribution, variations of the shear-wave velocity and geomechanical properties (Young's modulus and Poisson's ratio) were estimated by applying the advanced seismic interpretation techniques in the normal faults domain. The results show that the intensity of fractures increases once they are introduced to the normal faults, especially in the central part of the study area around well#2. Such a fractured zone is verified by fracture density log derived from FMI logs of the mentioned well. Obviously, there is a close-knit relationship between the fracture system and the normal faults. Eventually, secondary porosity caused by features was determined though identification of Hydraulic Flow Units (HFUs). Based on the porosity and permeability data, seven HFUs were determined for the Ilam-Sarvak reservoirs. The very high values of Log FZI indicate the possible presence of fractures. Overall, the fractures contributed to enhance the secondary porosity of the reservoir rocks though increasing matrix permeability. To sum up, the fractures system plays a critical role in controlling reservoir properties especially in the hanging-wall of normal faults where the majority of the macro and micro fractures are distributed

Breakouts derived from image logs aid the estimation of maximum horizontal stress: A case study from Perth Basin, Western Australia

 In-situ stresses are highly important for wellbore stability studies during drilling, completion and production. Different methods are available to estimate the horizontal stresses especially maximum horizontal stress. Typically, Circumferential Borehole Image Logs can be run to determine the direction and width of breakouts and then stresses at different depths based on the equation developed by Barton et al. (1988). This research focuses on image logs from Harvey-1 well located in the Southern Perth basin to compare the maximum horizontal stresses obtained by various methods. The magnitudes of stresses from the breakout width approach (Barton’s method) exhibit a considerable offset in comparison with elastic methods. Further investigations show that the likely reason for the offset relates to the fundamental assumption of the breakout width approach in which shear failures are considered to be constrained to horizontal planes. Failures within the wellbore are not necessarily horizontal and can be developed in different non-planar trajectories with various angles to the horizontal plane. Furthermore, the possible in-situ stresses from regional studies are constrained by means of stress polygons against which the reliability of results from breakout methods can be checked. Results indicate that due diligence and special care must be exercised for determination of maximum stresses from breakouts and more reliable methods are required than those currently used.Cited as: Faraji, M., Rezagholilou, A., Ghanavati, M., Kadkhodaie, A., Wood, D. A. Breakouts derived from image logs aid the estimation of maximum horizontal stress: A case study from Perth Basin, Western Australia. Advances in Geo-Energy Research, 2021, 5(1): 8-24, doi: 10.46690/ager.2021.01.0