The Pinghu Formation is a low permeability sandstone reservoir in the KQT Region, East China Sea. Its porosity ranges from 3.6 to 18.0%, and permeability is distributed from 0.5 to 251.19 mD. The relationship between porosity and permeability was poor due to strong heterogeneity. This led to the difficulty of quantitatively evaluating effective reservoirs and identifying pore fluids by using common methods. In this study, to effectively evaluate low permeability sandstones in the Pinghu Formation of KQT Region, pore structure was first characterized from nuclear magnetic resonance (NMR) logging based on piecewise function calibration (PFC) method. Effective formation classification criteria were established to indicate the “sweet spot”. Afterwards, several effective methods were proposed to calculate formation of petrophysical parameters, e.g., porosity, permeability, water saturation (S_w), irreducible water saturation (S_wirr). Finally, two techniques, established based on the crossplots of mean value of apparent formation water resistivity (R_wam) versus variance of apparent formation water resistivity (R_wav)—S_w versus S_wirr—were adopted to distinguish hydrocarbon-bearing formations from water saturated layers. Field applications in two different regions illustrated that the established methods and techniques were widely applicable. Computed petrophysical parameters matched well with core-derived results, and pore fluids were obviously identified. These methods were valuable in improving low permeability sandstone reservoirs characterization.

Summary: Nuclear magnetic resonance (NMR) logging has been extensively utilized for identifying pore fluids in recent years. However, after oil-based mud (OBM) invasion, OBM filtrate partially takes the place of the original fluid in the reservoir and the morphology of the NMR T2 (transverse relaxation time) spectrum changes. OBM invasion makes it difficult to identify the formation fluid using routine NMR fluid identification methods. In this study, we took heterogeneous conglomerate formation in the northwestern Junggar Basin as a case study and carried out core experiments under four conditions (viz., water-saturated, OBM displacing water, oil-saturated, and OBM displacing oil) and simulated the states of OBM invasion into water and hydrocarbon-bearing formation. Comparative analysis finds that when the OBM invades the water layer, the movable peak of the T2 spectrum primarily reflects the bulk relaxation characteristics of OBM filtrate, whereas when the OBM invades into the oil layer, the T2 spectrum may exhibit a three-peak distribution, where the first peak mainly indicates irreducible water, the second peak reflects OBM filtrate, and the third peak primarily reflects nondisplaced oil. To facilitate fluid identification, two T2 cutoffs are adopted to divide the T2 spectrum into three segments, and combined with the T2 geometric average, a fluid identification factor (ifluid) is proposed. Finally, identification criteria for reservoir type are established on the NMR logging and drillstem test data. The field application verifies the reliability of the proposed methods. These methods realize the identification of oil layers under OBM drilling and guide the subsequent production and development of oil reservoirs.

Summary: Permeability is a crucial parameter in formation evaluation, which reflects reservoir fluid mobility and directly influences subsequent development and production. In conventional to tight sandstone formations without fractures, numerous methods have been developed to predict permeability. However, permeability prediction accuracy based on the current method is low in fractured tight sandstone reservoirs due to the influence of heterogeneity, and little research is focused on permeability evaluation in such reservoirs. Conventional wireline and nuclear magnetic resonance (NMR) logging lose their role in fracture parameter evaluation, whereas electrical imaging logging is available for reflecting fracture information. Hence, we adopt electrical imaging logging, instead of conventional wireline and NMR logging, to predict permeability in fractured tight sandstone reservoirs. In this study, we propose a morphological method for permeability prediction using electrical imaging logging. Initially, we process the electrical imaging logging data to obtain the porosity spectra and determine the peak of the primary porosity spectrum. We then extract the mean and standard deviation of the primary porosity spectrum based on its distribution morphology. Meanwhile, we also use the normal distribution function to fit the primary porosity spectrum and accumulate the amplitudes of the original and fitted porosity spectra, respectively. When the cumulative amplitude of the fitted spectra stabilizes, the corresponding porosity indicates the boundary between primary and secondary pores. This boundary allows us to calculate the proportion of primary pores to total pores. Permeability in fractured tight sandstone formations is influenced by both primary and secondary pores, showing a strong correlation with the proportion of primary porosity. Finally, we establish a permeability prediction model based on total porosity and the proportion of matrix pores. The reliability of our established permeability evaluation model is confirmed by comparing the predicted permeabilities with core-derived results in the Triassic Chang 63 Member of the Jiyuan area in the Ordos Basin. In unfractured formations, the predicted permeability based on our raised model closely matches the results derived solely from total porosity. In fractured formations, however, the calculated permeability using our proposed model aligns more closely with the core-derived result, while predictions based on current and NMR models tend to underestimate permeability.

The role of the horizontal well in developing unconventional oil and gas reservoirs is particularly significant. Different from vertical wells, horizontal wells are greatly affected by many factors, e.g., well track, surrounding mudstone, resistivity, and pore structure heterogeneity in horizontal and vertical directions. These make it difficult to evaluate reservoir parameters and determine optimized test layers. In order to improve formation evaluation in horizontal wells, it is necessary to carry out the research of analyzing formation anisotropy, predict physical property parameters, and classify formation to determine high-quality intervals. In this study, taking Triassic Chang 8 Formation in Shunning Region, Central Ordos Basin as an example, 40 core samples were drilled and applied for nuclear magnetic resonance (NMR), mercury injection capillary pressure (MICP), and resistivity experiments. The porosity, permeability, resistivity, and pore structure anisotropy are analyzed. Results illustrate that the physical properties and pore structure in horizontal direction are superior to vertical direction. Meanwhile, NMR log loses its role in pore structure characterization in horizontal wells. Afterward, methods of correcting interval transit time (DT) and resistivity anisotropy are raised, and models of predicting formation physical property parameters, such as porosity, permeability, and water saturation, are established. A parameter, named as the formation type indicator (FTI), is proposed to reflect reservoir oil-bearing properties and pore structure. Finally, our target horizontal intervals are classified into four types, and the highest-quality “sweet spot” is determined.

Numerical simulation of nuclear magnetic resonance (NMR) can simulate experimental scenarios and quantify the impact of each factor on the physical characteristics. However, general simulation methods lack authentic pore structure information and fail to accurately model the complex geometry of rocks. High-resolution digital rock cores can effectively reflect pore structure. In this paper, a high-resolution digital core of Berea sandstone is taken as the research object, the pore parameters of the core (e.g., pore volume and surface-to-volume ratio) are quantified, and the 12 529 pores extracted from the three-dimensional digital core are statistically analyzed. Subsequently, the pores are classified based on their surface-to-volume ratio and volume. After the simulation parameters are calibrated by the experiments, NMR response of different water-saturated pores is simulated. Finally, the NMR response of the core with different oil saturation is simulated. We find that the distribution of pore quantity in terms of volume and surface area both follows a power function. There is a strong correlation between pore volume and surface area, especially for smaller pores. The T2 (transverse relaxation time) spectrum can generally reflect the volume distribution, but it may not accurately reflect the volume distribution when the pores are large. We also observe that the spectrum peak reflecting oil bulk relaxation is positioned to the left of the peak of the oil bulk relaxation due to the combined effects of surface relaxation of residual water and diffusion relaxation. These simulation results provide a physical basis for interpreting NMR measurements and can help identify fluids in reservoirs.