Yujiang Shi

A Novel Model of Predicting Archie's Cementation Factor from Nuclear Magnetic Resonance (NMR) Logs in Low Permeability Reservoirs

The resistivity experimental measurements of core samples drilled from low permeability reservoirs of Ordos Basin, Northwest China, illustrate that the cementation factors are not agminate, but vary from 1.335 to 1.749. This leads to a challenge for the estimation of water and hydrocarbon saturation. Based on the analysis of Purcell equation and assumption that rock resistivity is determined by the parallel connection of numerous capillary resistances, a theoretical expression of cementation factor in terms of porosity and permeability is established. Then, cementation factor can be calculated if the parameters of porosity and permeability are determined. In the field application, porosity can be easily obtained by conventional logs. However, it is a tough challenge to estimate permeability due to the strong heterogeneity of low permeability reservoirs. Thus, the Schlumberger Doll Research (SDR) model derived from NMR logs has been proposed to estimate permeability. Based on the analysis of the theoretical expressions of cementation factor and SDR model, a novel cementation factor prediction model, which is relevant to porosity and logarithmic mean of NMR T2 spectrum (T2lm), is derived. The advantage of this model is that all the input information can be acquired from NMR logs accurately. In order to confirm the credibility of the novel model, the resistivity and corresponding laboratory NMR measurements of 27 core samples are conducted. The credibility of the model is confirmed by comparing the predicted cementation factors with the core analyzed results. The absolute errors for all core samples are lower than 0.071. Once this model is extended to field application, the accuracy of water and hydrocarbon saturation estimation will be significantly improved.

Comparative study of models for predicting permeability from nuclear magnetic resonance (NMR) logs in two Chinese tight sandstone reservoirs

Based on the analysis of mercury injection capillary pressure (MICP) and nuclear magnetic resonance (NMR) experimental data for core plugs, which were drilled from two Chinese tight sandstone reservoirs, permeability prediction models, such as the classical SDR, Timur-Coates, the Swanson parameter, the Capillary Parachor, the R10 and R35 models, are calibrated to estimating permeabilities from field NMR logs, and the applicabilities of these permeability prediction models are compared. The processing results of several field examples show that the SDR model is unavailable in tight sandstone reservoirs. The Timur-Coates model is effective once the optimal T2cutoff can be acquired to accurately calculate FFI and BVI from field NMR logs. The Swanson parameter model and the Capillary Parachor model are not always available in tight sandstone reservoirs. The R35 based model cannot effectively work in tight sandstone reservoirs, while the R10 based model is optimal in permeability prediction.

A new empirical method for constructing capillary pressure curves from conventional logs in low-permeability sandstones

Pore structure reflected from capillary pressure curves plays an important role in low-permeability formation evaluation. It is a common way to construct capillary pressure curves by Nuclear Magnetic Resonance (NMR) log. However, the method’s efficiency will be severely affected if there is no NMR log data or it cannot reflect pore structure well. Therefore, on the basis of J function and diagenetic facies classification, a new empirical model for constructing capillary pressure curves from conventional logs is proposed here as a solution to the problem. This model includes porosity and the relative value of natural gamma rays as independent variables and the saturation of mercury injection as a dependent variable. According to the 51 core experimental data sets of three diagenetic facies from the bottom of the Upper Triassic in the western Ordos Basin, China, the model’s parameters in each diagenetic facies are calibrated. Both self-checking and extrapolation tests show a positive effect, which demonstrates the high reliability of the proposed capillary pressure curve construction model. Based on the constructed capillary pressure curves, NMR T2 spectra under fully brine-saturated conditions are mapped by a piecewise power function. A field study is then presented. Agreement can be seen between the mapped NMR T2 spectra and the MRIL-P log data in the location of the major peak, right boundary, distribution characteristics and T2 logarithmic mean value. In addition, the capillary pressure curve construction model proposed in this paper is not affected by special log data or formation condition. It is of great importance in evaluating pore structure, predicting oil production and identifying oil layers through NMR log data in low-permeability sandstones.

Predicting reservoir wettability via well logs

Wettability is an important factor in controlling the distribution of oil and water. However, its evaluation has so far been a difficult problem because no log data can directly indicate it. In this paper, a new method is proposed for quantitatively predicting reservoir wettability via well log analysis. Specifically, based on the J function, diagenetic facies classification and the piecewise power functions, capillary pressure curves are constructed from conventional logs and a nuclear magnetic resonance (NMR) log respectively. Under the influence of wettability, the latter is distorted while the former remains unaffected. Therefore, the ratio of the median radius obtained from the two kinds of capillary pressure curve is calculated to reflect wettability, a quantitative relationship between the ratio and reservoir wettability is then established. According to the low-permeability core sample capillary pressure curve, NMR spectrum and contact angle experimental data from the bottom of the Upper Triassic reservoirs in western Ordos Basin, China, two kinds of constructing capillary pressure curve models and a predictive wettability model are calibrated. The wettability model is verified through the Amott wettability index and saturation exponent from resistivity measurement and their determined wettability levels are comparable, indicating that the proposed model is quite reliable. In addition, the models good application effect is exhibited in the field study. Thus, the quantitatively predicting reservoir wettability model proposed in this paper provides an effective tool for formation evaluation, field development and the improvement of oil recovery.