A new correlation for water saturation calculation in gas shale reservoirs based on compensation of kerogen-clay conductivity

Determination of water saturation in gas shale reservoirs is a very challenging issue due to the incomplete understanding of the non-Archie components. Kerogen and clay content are the two main factors controlling the conductivity of gas shales and resistivity log responses. The presence of clays as conductive materials causes excessive conductivity for the rock that result in an overestimation of water saturation calculation. On the other hand, the presence of solid kerogen has an opposite effect to clays and causes reduction of rock conductivity and thus underestimation of water saturation.In this research, attempts have been made to develop an effective equation for water saturation determination in gas shale reservoirs based on compensation of kerogen and shale conductivities. The new equation is able to handle both high and low conductivity components. The proposed approach makes one step ahead towards reducing uncertainty in the petrophysical evaluation of gas shale reservoirs. Being independent of formation water resistivity and Archie parameters are of the important and effective aspects of the introduced equation in water saturation calculation of gas shale reservoirs.Finally, the kerogen-clay compensation equation has successfully been applied to the determination of water saturation in the Goldwyer shale formation, Canning basin, Western Australia

Prediction of shear wave velocity from petrophysical data utilizing intelligent systems: An example from a sandstone reservoir of Carnarvon Basin, Australia

Shear wave velocity associated with compressional wave velocity can provide the accurate data for geophysical study of a reservoir. These so called petroacoustic studies have important role in reservoir characterization such as lithology determination, identifying pore fluid type, and geophysical interpretation. In this study, a fuzzy logic, a neuro-fuzzy and an artificial neural network approaches were used as intelligent tools to predict shear wave velocity from petrophysical data. The petrophysical data of two wells were used for constructing intelligent models in a sandstone reservoir of Carnarvon Basin, NW Shelf of Australia. A third well of the field was used to evaluate the reliability of the models. The results show that intelligent models have been successful for prediction of shear wave velocity from conventional well log data

A Committee Machine with Intelligent Systems for Estimation of Total Organic Carbon Content from Petrophysical Data: an Example from Kangan and Dalan Reservoirs in South Pars Gas Field, Iran

Total Organic Carbon (TOC) content present in reservoir rocks is one of the important parameters which could be used for evaluation of residual production potential and geochemical characterization of hydrocarbon bearing units. In general, organic rich rocks are characterized by higher porosity, higher sonic transit time, lower density, higher gamma-ray, and higher resistivity than other rocks. Current study suggests an improved and optimal model for TOC estimation by integration of intelligent systems and the concept of committee machine with an example from Kangan and Dalan Formations, in South Pars Gas Field, Iran. This committee machine with intelligent systems (CMIS) combines the results of TOC predicted from intelligent systems including fuzzy logic (FL), neuro-fuzzy (NF), and neural network (NN), each of them has a weight factor showing its contribution in overall prediction. The optimal combination of weights is derived by a genetic algorithm (GA). This method is illustrated using a case study. One hundred twenty-four data points including petrophysical data and measured TOC from three wells of South Pars Gas Field were divided into eighty-seven training sets to build the CMIS model and thirty-seven testing sets to evaluate the reliability of the developed model. The results show that the CMIS performs better than any one of the individual intelligent systems acting alone for predicting TOC

Petrophysical data prediction from seismic attributes using committee fuzzy inference system

This study presents an intelligent model based on fuzzy systems for making aquantitative formulation between seismic attributes and petrophysical data. The proposed methodology comprises two major steps. Firstly, the petrophysical data, including water saturation (Sw) and porosity, are predicted from seismic attributes using various Fuzzy Inference Systems (FIS), including Sugeno (SFIS), Mamdani (MFIS) and Larsen (LFIS). Secondly, a Committee Fuzzy Inference System (CFIS) is constructed using a hybrid Genetic Algorithms-Pattern Search (GA-PS) technique. The inputs of the CFIS model are the output averages of theFIS petrophysical data. The methodology is illustrated using 3D seismic and petrophysical data of 11 wells of an Iranian offshore oil field in the Persian Gulf. The performance of the CFIS model is compared with a Probabilistic Neural Network (PNN). The results show that the CFIS method performed better than neural network, the best individual fuzzy model and a simple averaging method

A committee neural network for prediction of normalized oil content from well log data: An example from South Pars Gas Field, Persian Gulf

Normalized oil content (NOC) is an important geochemical factor for identifyingpotential pay zones in hydrocarbon source rocks. The present study proposes an optimaland improved model to make a quantitative and qualitative correlation between NOC andwell log responses by integration of neural network training algorithms and thecommittee machine concept. This committee machine with training algorithms (CMTA)combines Levenberg-Marquardt (LM), Bayesian regularization (BR), gradient descent(GD), one step secant (OSS), and resilient back-propagation (RP) algorithms. Each ofthese algorithms has a weight factor showing its contribution in overall prediction. Theoptimal combination of the weights is derived by a genetic algorithm. The method isillustrated using a case study. For this purpose, 231 data composed of well log data andmeasured NOC from three wells of South Pars Gas Field were clustered into 194modeling dataset and 37 testing samples for evaluating reliability of the models. Theresults of this study show that the CMTA provides more reliable and acceptable resultsthan each of the individual neural networks differing in training algorithms. Also CMTAcan accurately identify production pay zones (PPZs) from well logs

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

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

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

Estimation of the Petrophysical Properties of the Lower Cretaceous Yamama (YC) Formation in Siba Field

   In southern Iraq, the Yamama Formation has been a primary carbonate resource since the Lower Cretaceous era. This study covers Siba Field, which is located in southeastern Iraq. This paper will be devoted to a YC unit of study. The most crucial step in reservoir management is petrophysical characterization. The primary goal of this research is to assess the reservoir features and lithology of the Yamama (YC) Formation in the Siba region. Accessible excellent logs include sonic, density, neutron, gamma-ray, SP, and resistivity readings. The Interactive Petrophysics (IP4.4) program examined and estimated petrophysical features such as clay volume, porosity, and water saturation. The optimum approach was the neutron density and clay volume calculation using the Gamma Ray Method (VclGR), it was 0.246 in SB-6 since they are not impacted by anything. The Archie method was chosen due to its suitability for limestone. The lithology and mineralogy of the formations were determined using M-N cross plots; the diagram revealed that the Formation was composed of limestone. The Archie parameter was determined using the Pickett plot and formation water resistivity from the Pickett plot and SP log where the results were similar in all wells (RW=0.016, m=2.08, n=2.3, a=1.1). In addition, the higher section of the formation has good reservoir qualities such as density is (2.368g/cc), porosity is (PHIE=0.117) in SB-6

13C-direct detected NMR experiments for the sequential J-based resonance assignment of RNA oligonucleotides

We present here a set of 13C-direct detected NMR experiments to facilitate the resonance assignment of RNA oligonucleotides. Three experiments have been developed: (1) the (H)CC-TOCSY-experiment utilizing a virtual decoupling scheme to assign the intraresidual ribose 13C-spins, (2) the (H)CPC-experiment that correlates each phosphorus with the C4′ nuclei of adjacent nucleotides via J(C,P) couplings and (3) the (H)CPC-CCH-TOCSY-experiment that correlates the phosphorus nuclei with the respective C1′,H1′ ribose signals. The experiments were applied to two RNA hairpin structures. The current set of 13C-direct detected experiments allows direct and unambiguous assignment of the majority of the hetero nuclei and the identification of the individual ribose moieties following their sequential assignment. Thus, 13C-direct detected NMR methods constitute useful complements to the conventional 1H-detected approach for the resonance assignment of oligonucleotides that is often hindered by the limited chemical shift dispersion. The developed methods can also be applied to large deuterated RNAs