Appraisal of intra-reservoir barriers in the Permo-Triassic successions of the Central Persian Gulf, Offshore Iran

Owing to their tightness, intra reservoir barriers have the potential to prevent homogenization of reservoir fluids and so cause compartmentalization. Identification of these barriers is an important step during reservoir evaluation. In order to achieve this, three main approaches: i) detailed petrographic and core analysis, ii) petrophysical studies (flow unit concept) and iii) geochemical analysis (strontium residual salt analysis) were applied systematically in the Permo-Triassic carbonate reservoirs (Dalan and Kangan formations) of a supergiant gas reservoir located in the Central Persian Gulf. Integration of these approaches has led to a fullclarification of the intra reservoir barriers. Petrographic examinations revealed the potential stratigraphic barriers to fluids flow created by various depositional/ diagenetic characteristics. Petrophysical data such as poroperm values, pore throat size distribution and scanning electron microscopy (SEM) analysis were used to differentiate the reservoir flow units from non-reservoir rock. According to different trends in 87Sr/86Sr ratios of residual salts, the existence of flow barriers was evaluated and proved. Finally, by integrating these approaches, three intra reservoir barriers were introduced in the studied reservoir interval. These intra reservoir barriers are depositional and diagenetic in nature and are located in stratal positions with sequence stratigraphic significance. The possibility of reservoir compartmentalization was evaluated in the studied wells, and then their existence was predicted at the adjacent fields. As shown in this study, integration of petrographic examinations with flow unit determination in a sequence stratigraphic framework has the potential for recognizing intra reservoir barriers and predicting compartmentalization of the studied Permo-Triassic reservoirs

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

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

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 seismic-driven 3D model of rock mechanical facies: An example from the Asmari reservoir, SW Iran

Asmari Formation is one of the most prolific and important hydrocarbon reservoirs in Iran. This formation in the Cheshmeh-Khosh oilfield shows mixed carbonate-siliciclastic lithology and its elastic modulus changes are correlatable with facies changes. To address these changes, we investigated the relation between sedimentary environment (facies) and texture with various elastic moduli. The Young's modulus shows higher correlation with the facies changes. Data from three wells are analyzed and used for the construction of rock mechanical facies. Based on elastic properties, facies and texture changes as well as petrophysical characteristics seven rock mechanical facies (RMFs) are recognized in the studied formation. To predict RMFs at inter-well spaces more efficiently and capturing the lateral formation property variationsa 3D rock mechanical facies model is constructed based on seismic attributes. In this method, RMFs are correlatable between the studied wells and mappable by seismic attribute in the field scale. Finally, the distribution of RMFs and their related properties is investigated in the studied field