جستجوی مقالات مرتبط با کلیدواژه "nmr" در نشریات گروه "مهندسی شیمی، نفت و پلیمر"

Determining and estimating reservoir properties such as capillary pressure and relative permeability are always challenging in reservoir studies. The current research has been done in the Sarvak Formation in the southwestern oilfields of Iran, focusing on applying Nuclear Magnetic Resonance (NMR) logging in synthesizing capillary pressure and relative permeability. In this study, the Sarvak Formation in the studied well was divided into three flow units using the Hydraulic Flow Unit method. Then, for each separate flow unit, the capillary pressure curves were calculated and illustrated from the NMR T2 distribution data. Next, the calculated capillary pressure curves were corrected concerning the laboratory capillary pressure data derived from SCAL. Then, the conversion factor to calibrate well log-derived Pc curves to SCAL Pcs was determined for each flow unit. Afterward, mathematical functions for capillary pressure curve estimation were written using MATLAB software. Next, the relative permeability for oil and water phases was calculated and plotted in MATLAB software based on the functions determined in the previous step. Finally, the representative capillary pressure curve and relative permeability curve of the oil and water phases were determined for each flow unit. The results showed an increasing trend in the reservoir quality from flow units 1 to 3. Flow unit 3 had the lowest capillary pressure with the highest relative permeability and, consequently, better reservoir quality. In contrast, flow unit 1 had the highest PC levels, the lowest relative permeability, and the lowest reservoir quality.

A detailed description of the carbonate reservoir is an important step in preparing a field development plan. An accurate determination of petrophysical parameters and rock characteristics are key parameters in the carbonate reservoir description. The rock properties are traditionally obtained from different techniques such as lab measurement, well logging, well test, etc. In this manuscript, data from core measurements and NMR measurements are analyzed to study the petrophysical properties of Cretaceous carbonate rock from Asmari Formation. First, the pore size, pore system, porosity and permeability are determined from the core measurements and NMR Analysis. Second, the results of core and NMR evaluations are compared, and the reasons for differences are distinguished. Comparison between the porosity values demonstrates that porosity from NMR and helium injection experiments are very similar in which the average porosity is 21.4 % and NMR porosity is 20.68%. Afterwards, pore sizes received from the NMR model show reliable results and match the pore size distribution determined from the MICP experiment. The permeability value is modeled with NMR permeability predicting models, namely Standard Kenyon and Timur-Coates. Adjusted NMR Permeability results are 17.7 (mD) and 18 (mD) for (SDR) and (TC) methods, respectively, and they are consistent with laboratory core permeability results (Kg=22, Kl=19.2, Kw=18.4). The pore throat distributions are also similar for two NMR and core measurement methods. This study shows how NMR analysis could be useful in determining petrophysical parameters. Ultimately, the results for reservoir characteristics of carbonate rock obtained by core and NMR experiments are compared quantitatively and qualitatively.

In particular, quantitative laboratory measurements are challenging to perform due to their costs and time consumption. So, the need to explore other available data interconnectivity to permeability is of great importance. One of these data is NMR (Nuclear Magnetic Resonance) log data which have been used frequently in recent studies. It is considered to segregate different groups, which can be obtained through cluster analysis. Using reliable parameters in the cluster analysis helps to segregate different rock units which can be used in the permeability models. To select reliable parameters, cross plots of the permeability versus extracted features from the NMR T2 distribution curve were plotted. Results indicate that TCMR, peak reading amplitudes, and T2Lm are the best permeability indicators, respectively. Cluster analysis was performed on T2LM, TCMR and Peak reading amplitudes, which showed the highest consistency with core derived data compared with other parameters. The crucial step is to determine the best estimate of the number of clusters. It is usually taken as a prior in most clustering algorithms. In this research, Davies-Bouldin criterion values versus the number of clusters were used to obtain the optimal number of clusters. The knee method, which finds the “knee” in many clusters vs. clustering evaluation graph, was used. A clustering model with the number of clusters from 2-100 was created. It showed the five is an optimal number of clusters. Subsequently, the Schlumberger-Doll-Research (SDR) coefficients for each cluster were modified using a curve fitting tool in the Matlab software. Results indicated that calculated permeability using cluster analysis showed a higher correlation by core derived permeability than the original SDR permeability model. Since this is the core part of the group attempt to use extracted T2 distribution features in permeability estimation in carbonate reservoirs, more investigation is required to attempt satisfactory results to standardize the value of the coefficient of the permeability models in carbonate rocks with different petrophysical properties.

Permeability is arguably the most critical property for evaluating flow in the reservoir. It is also one of the challenging parameters which must be measured in the field. Nuclear Magnetic Resonance (NMR) logging across the borehole is among the popular techniques, which it is utilized to determine permeability across the reservoir. However, available correlations in literature for estimating permeability from NMR data do not usually provide acceptable accuracy in the carbonate rocks. Therefore, a new model is proposed to estimate permeability by establishing a relationship between core derived permeability and extracted features from the T2 distribution curve of NMR data with the ensemble LSBoost algorithm. The feature extraction process is performed using peak analysis on T2 distribution curves which it leads to 5 relevant parameters, including T2lm, TCMR, prominence, peak amplitude and width. The proposed model is validated by comparing the proposed method’s correlation coefficient against Timur-Coates and SDR equation estimation accuracy. The results show that our model generally provides better prediction accuracies in comparison with the empirical equation-based derived permeabilities.

Since the development bloom in unconventional reservoirs in North America, total organic carbon (TOC) has become a more essential parameter, as the indicator of the efficiency of these reservoirs. In this paper, by using conventional well logs and NMR log data, the TOC content of an unconventional reservoir in West Africa is estimated. Passy’s, Issler’s, and Schmoker’s methods were used as indirect wireline methods to estimate TOC content, along the well paths. Afterward, NMR log data, as a direct method, was used to provide more precise calculations of TOC. Both methods showed almost similar trends, with the NMR method indicating lower values for the TOC. Then, an adjusted Schmoker equation was proposed, which showed the best fit between NMR and conventional well logs results. By using the equation, the TOC content was calculated in three other wells, where NMR data were unavailable. The results were then used to prepare a 3D model of the TOC distribution, within the reservoir.