Direct hydrocarbon identification in shale oil reservoirs using fluid dispersion attribute based on an extended frequency-dependent seismic inversion scheme

The identification of hydrocarbons using seismic methods is critical in the prediction of shale oil res-ervoirs.However,delineating shales of high oil saturation is challenging owing to the similarity in the elastic properties of oil-and water-bearing shales.The complexity of the organic matter properties associated with kerogen and hydrocarbon further complicates the characterization of shale oil reservoirs using seismic methods.Nevertheless,the inelastic shale properties associated with oil saturation can enable the utilization of velocity dispersion for hydrocarbon identification in shales.In this study,a seismic inversion scheme based on the fluid dispersion attribute was proposed for the estimation of hydrocarbon enrichment.In the proposed approach,the conventional frequency-dependent inversion scheme was extended by incorporating the PP-wave reflection coefficient presented in terms of the effective fluid bulk modulus.A rock physics model for shale oil reservoirs was constructed to describe the relationship between hydrocarbon saturation and shale inelasticity.According to the modeling results,the hydrocarbon sensitivity of the frequency-dependent effective fluid bulk modulus is superior to the traditional compressional wave velocity dispersion of shales.Quantitative analysis of the inversion re-sults based on synthetics also reveals that the proposed approach identifies the oil saturation and related hydrocarbon enrichment better than the above-mentioned conventional approach.Meanwhile,in real data applications,actual drilling results validate the superiority of the proposed fluid dispersion attribute as a useful hydrocarbon indicator in shale oil reservoirs.

Construction of well logging knowledge graph and intelligent identification method of hydrocarbon-bearing formation

Based on the well logging knowledge graph of hydrocarbon-bearing formation(HBF),a Knowledge-Powered Neural Network Formation Evaluation model(KPNFE)has been proposed.It has the following functions:(1)extracting characteristic parameters describing HBF in multiple dimensions and multiple scales;(2)showing the characteristic parameter-related entities,relationships,and attributes as vectors via graph embedding technique;(3)intelligently identifying HBF;(4)seamlessly integrating expertise into the intelligent computing to establish the assessment system and ranking algorithm for potential pay recommendation.Taking 547 wells encountered the low porosity and low permeability Chang 6 Member of Triassic in the Jiyuan Block of Ordos Basin,NW China as objects,80%of the wells were randomly selected as the training dataset and the remainder as the validation dataset.The KPNFE prediction results on the validation dataset had a coincidence rate of 94.43%with the expert interpretation results and a coincidence rate of 84.38%for all the oil testing layers,which is 13 percentage points higher in accuracy and over 100 times faster than the primary conventional interpretation.In addition,a number of potential pays likely to produce industrial oil were recommended.The KPNFE model effectively inherits,carries forward and improves the expert knowledge,nicely solving the robustness problem in HBF identification.The KPNFE,with good interpretability and high accuracy of computation results,is a powerful technical means for efficient and high-quality well logging re-evaluation of old wells in mature oilfields.

Exposure measurement, risk assessment and source identification for exposure of traffic assistants to particle-bound PAHs in Tianjin, China

To investigate the levels of exposure to particulate-bound polycyclic aromatic hydrocarbon （PAH） and to estimate the risk these levels pose to traffic assistants （TAs） in Tianjin （a rnegacity in North China）, a measurement campaign （33 all-day exposure samples, 25 occupational-exposure samples and 10 indoor samples） was conducted to characterize the TAs＇ exposure to PAHs, assess the cancer risk and identify the potential sources of exposure. The average total exposure concentration of 14 PAHs was approximately 2871 ＋ 928 ng/rn3 （on-duty）, and 1622 ＋ 457 ng/m3 （all-day）. The indoor PAHs level was 1257 ＋ 107 ng/m3. After 8000 Monte Carlo simulations, the cancer risk resulting from exposure to PAHs was found to be approximately 1.05 x 10-4. A multivariate analysis was applied to identify the potential sources, and the results showed that, in addition to vehicle exhaust, coal combustion and cooking fumes were also another two important contributors to personal PAH exposure. The diagnostic ratios of PAH compounds agree with the source apportionment results derived from principal component analysis.