Conductivity mechanism in low-resistivity gas hydrate reservoirs

The present dataset provides the underlying information for the digital rock-based investigation of the conductivity mechanism in low-resistivity gas hydrate reservoirs in the Muli permafrost in China.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Deep Coalbed Methane Seams' Characterization

Segmentation of CT images from the Deep Coalbed Methane Seams rock samples using deep learning neural networksTHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Evaluating the effect of new gas solubility and bubble point models on PVT parameters and optimizing injected gas rate in gas-lift dual gradient drilling

Gas-lift dual gradient drilling (DGD) is a solution for the complex problems caused by the narrow drilling window in deepwater drilling. However, investigations are lacking on using oil-based drilling fluid in DGD, which is the principal novel idea of the present study. Herein, Nitrogen was selected as the injection gas into the riser. This research compares the results obtained from two new models with those obtained from the Standing correlations for solubility and bubble point pressure. Specifically, the study evaluates the PVT behaviors of drilling fluid (oil/water/nitrogen) in gas-lift dual gradient drilling operations in the case of using the new models or the Standing correlations by coding in MATLAB environment. Standing correlation is one of the conventional Black oil models; however, it overestimates the solubility of Nitrogen and underestimates the bubble pressure point in white mineral oil. According to the achieved results, the Standing model has some errors in evaluating the PVT behavior of Nitrogen and oil-based drilling fluids and is not recommended for the mixtures in the gas-lift dual gradient drilling. With regard to optimizing gas flow rate, it was found that the discrepancy between pressures for the new models and the Standing models is higher at both high liquid flow rates and at lower gas flow rates.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Synthesis of capillary pressure curves from post stack seismic data with the use of intelligent estimators: A case study from the Iranian part of the South Pars gas field, Persian Gulf Basin

Capillary pressure curves are important data for reservoir rock typing, analyzing pore throat distribution, determining height above free water level, and reservoir simulation. Laboratory experiments provide accurate data, however they are expensive, time-consuming and discontinuous through the reservoir intervals. The current study focuses on synthesizing artificial capillary pressure (Pc) curves from seismic attributes with the use of artificial intelligent systems including Artificial Neural Networks (ANNs), Fuzzy logic (FL) and Adaptive Neuro-Fuzzy Inference Systems (ANFISs). The synthetic capillary pressure curves were achieved by estimating pressure values at six mercury saturation points. These points correspond to mercury filled pore volumes of core samples (Hg-saturation) at 5%, 20%, 35%, 65%, 80%, and 90% saturations. To predict the synthetic Pc curve at each saturation point, various FL, ANFIS and ANN models were constructed. The varying neural network models differ in their training algorithm. Based on the performance function, the most accurately functioning models were selected as the final solvers to do the prediction process at each of the above-mentioned mercury saturation points. The constructed models were then tested at six depth points of the studied well which were already unforeseen by the models. The results show that the Fuzzy logic and neuro-fuzzy models were not capable of making reliable estimations, while the predictions from the ANN models were satisfyingly trustworthy.The obtained results showed a good agreement between the laboratory derived and synthetic capillary pressure curves. Finally, a 3D seismic cube was captured for which the required attributes were extracted and the capillary pressure cube was estimated by using the developed models. In the next step, the synthesized Pc cube was compared with the seismic cube and an acceptable correspondence was observed

Estimating NMR T2 distribution data from well log data with the use of a committee machine approach: A case study from the Asmari formation in the Zagros Basin, Iran

The Nuclear Magnetic Resonance (NMR) log is one of the most valuable logs in petroleum exploration which is used to precisely evaluate the reservoir and non-reservoir horizons. Along with porosity logs (neutron, density, sonic), NMR log is used to estimate the porosity and permeability of the hydrocarbon bearing intervals. The current study focuses on estimating NMR T2 distribution data from conventional well log data with the use of artificial intelligent systems. The eight bin porosities of the combinable magnetic resonance (CMR) T2 distribution alongside with the T2 logarithmic mean (T2LM) values are predicted using the intelligent models developed in this study. The methodology applied here combines the results of the individual models in a committee machine with intelligent systems (CMIS) for estimating the NMR T2 distribution and T2 logarithmic mean data. The Fuzzy logic (FL), the adaptive neuro fuzzy system (ANFIS) and artificial neural networks (ANNs) are utilized as intelligent experts of the CMIS. The NN models are developed with four different training algorithms (Levenberg–Marquardt (LM), scaled conjugate gradient (SCG), one step secant (OSS) and resilient back-propagation (RP)) and the best one is chosen as the optimal NN expert of the CMIS. The CMIS assigns a weight factor to each individual expert by the simple averaging and weighted averaging methods. A genetic algorithm (GA) optimization technique is used to derive the weighted averaging coefficients. The results indicate that the GA optimized CMIS performs better than the individual experts acting alone for synthesizing the NMR T2 curve and T2LM data from one specific set of conventional well logs

Construction of synthetic capillary pressure curves from the joint use of NMR log data and conventional logs

Capillary pressure (Pc) curves are important petrophysical parameters to characterize reservoir rock properties in hydrocarbon fields. Determination of Pc values conventionally relies on a variety of experimental processes. Although the experiments provide accurate outcomes, they may be extensive, time consuming and discontinues through the reservoir interval. The current study demonstrates the feasibility of synthesizing capillary pressure curves in carbonate reservoirs from conventional and Nuclear Magnetic Resonance (NMR) logs by using a two-step approach. The first step is to simulate T2 (longitude relaxation time) distribution values from conventional logs by using intelligent systems. For this purpose, eight Combinable Magnetic Resonance Bin Porosities (CBPs) are estimated from well logs with a reasonable accuracy (Correlation Coefficient (CC)>0.90 for almost all CBPs). In the second step, the Pc values are predicted from CBPs through an inversion process. The simulated Pc curves show a good agreement with laboratory derived Mercury Injection Capillary Pressure (MICP) curves at low mercury saturations (0.70) at different mercury saturations

The Joint Application of Diagenetic, Petrophysical and Geomechanical Data for Selecting Hydraulic Fracturing Candidate Zone: A Case Study from a Carbonate Reservoir in Iran

The more comprehensive information on the reservoir properties will help to better plan drilling and design production. Herein, diagenetic processes and geomechanical properties are notable parameters that determine reservoir quality. Recognizing the geomechanical properties of the reservoir as well as building a mechanical earth model play a strong role in the hydrocarbon reservoir life cycle and are key factors in analyzing wellbore instability, drilling operation optimization, and hydraulic fracturing designing operation. Therefore, the present study focuses on selecting the candidate zone for hydraulic fracturing through a novel approach that simultaneously considers the diagenetic, petrophysical, and geomechanical properties. The diagenetic processes were analyzed to determine the porosity types in the reservoir. After that, based on the laboratory test results for estimating reservoir petrophysical parameters, the zones with suitable reservoir properties were selected. Moreover, based on the reservoir geomechanical parameters and the constructed mechanical earth model, the best zones were selected for hydraulic fracturing operation in one of the Iranian fractured carbonate reservoirs. Finally, a new empirical equation for estimating pore pressure in nine zones of the studied well was developed. This equation provides a more precise estimation of stress profiles and thus leads to more accurate decision-making for candidate zone selection. Based on the results, vuggy porosity was the best porosity type, and zones C2, E2 and G2, having suitable values of porosity, permeability, and water saturation, showed good reservoir properties. Therefore, zone E2 and G2 were chosen as the candidate for hydraulic fracturing simulation based on their E (Young’s modulus) and ν (Poisson’s ratio) values. Based on the mechanical earth model and changes in the acoustic data versus depth, a new equation is introduced for calculating the pore pressure in the studied reservoir. According to the new equation, the dominant stress regime in the whole well, especially in the candidate zones, is SigHmax>SigV>Sighmin, while according to the pore pressure equation presented in the literature, the dominant stress regime in the studied well turns out to be SigHmax>Sighmin>SigV.