The ultra-low permeability oil reservoir in the HQ oilfield within the Ordos Basin exemplifies a classic “three-low” oil reservoir characterized by low pressure, low permeability, and low abundance. Upon the commencement of oil well production, substantial initial production decline and inadequate pressure maintenance levels are encountered. Consequently, these wells commonly face persistent low production issues resulting from ineffective water injection. Currently, the available technical approaches for repeated stimulation of such wells to enhance production and efficiency are limited, thereby restricting the effective utilization of the remaining oil reserves. In response to this challenge, this article presents an innovative technology tailored for high-efficiency re-fracturing to improve oil recovery in ultra-low permeability reservoirs. Grounded in the theory of multiple flow couplings and volume development, this technology introduces a novel integrated process encompassing seepage field reconstruction, fracturing, and oil displacement. This comprehensive approach culminates in an integrated energy replenishment methodology applicable throughout the entire reservoir’s life cycle. The proposed technology materializes a novel development method for ultra-low permeability reservoirs, centered on the principles of “seepage field reconstruction, integration of fracturing and oil displacement, multi-mode energy replenishment, and all-round displacement”. This integrated strategy ensures the efficient development of ultra-low permeability reservoirs. The successful implementation of this technology in the HQ oilfield is a notable achievement. Notably, the daily oil production of directional wells and horizontal wells significantly increased from 0.5 tons and 1.8 tons to 2.4 tons and more than 10 tons, respectively. Furthermore, the estimated ultimate recovery rate witnessed a substantial improvement from 5.2% to 17.3%. These compelling results underscore the potential of this technology in revitalizing the production of low-yield wells in ultra-low permeability reservoirs.

The identification of reservoir fractures is essential as it is an important factor in the design of a field development plan, which in turn affects the efficiency of hydrocarbon production. Water flooding and water channeling are serious due to the lack of objective understanding of the fracture development pattern in the tight oil reservoirs in the Triassic Change 6 member of Y well area, Ordos Basin, China. In this paper, we observed 104.6 m cores from 20 wells with a number of 150 fractures and an outcrop profile, then analyzed the main controlling factors for core fracture development, and finally established a fracture prediction method using conventional logging data. The results indicate that high-angle fractures accounted for 73.20%, fracture orientations were nearly east–west, fracture spacing between 0–10 cm accounted for 80.51%, fracture openings between 0–0.13 mm accounted for 89.27%, fracture down-cutting depths between 0–20 cm accounted for 80%, and 80.81% was not filled. In addition, we found that thin beds and fine sandstones are prone to develop fractures; Finally, our modified curve rate method was an effective method for fracture prediction. We conclude that fractures have the characteristics of high angle, small spacing, small opening, small down-cutting depth, and less filling. The modified curve change rate method is suitable for fracture prediction in tight sandstone reservoirs in the Triassic Change 6 member of the Y well area, Ordos Basin, China.

Despite the discovery of high-producing natural gas reservoirs in the low-permeability dolomite-mottled limestone (DML) reservoir of the fourth Member (Ma4) of the Majiagou Formation in the Ordos Basin, the current understanding of the processes responsible for reservoir formation are still superficial, which extremely restricts the effectiveness of deep petroleum exploration and development in the basin. Therefore, this study analyzed the paragenesis process of the DML reservoir through systematic petrographic and geochemical measurements. The DML consists of burrows and matrix. The burrows are mainly filled with dolomite with a small amount of micrite, calcite cement, and solid bitumen. The matrix mainly consists of wakestone or mudstone. The DML has experienced multiple diagenetic events, including seepage-reflux dolomitization, compaction, calcite cement CaI cementation, micrite recrystallization, dissolution, hydrocarbon charging, calcite cement CaII cementation, and dolomite progressive recrystallization. Dolomitization is critical to the DML reservoir formation. The pore created by dolomitization is the hydrocarbon-migrated pathway and storage space. Due to the difference in Mg²⁺-rich fluid supply, the degree of dolomitization decreases from west to east, which causes the difference in diagenetic evolution of the western and eastern parts of the study area. The high dolomitization degree led to strong anti-compaction ability in the west, contrary to the east. Thus, the reservoir quality of the west is better than the east.

The identification and prediction of petrofacies plays a crucial role in the study of shale oil and gas “sweet spots”. However, the petrofacies identified through core and core test data are not available for all wells. Therefore, it is essential to establish a petrofacies identification model using conventional well logging data. In this study, we determined the petrofacies of shale oil reservoirs in the Upper Triassic Yanchang Formation, Ordos Basin, China, based on scanning electron microscopy, core porosity and total organic carbon (TOC), and brittleness index calculations from X-ray diffraction (XRD) experiments conducted on seven members of the formation. Furthermore, we compared the interpreted logs with the raw well logs data clustered into electrofacies in order to assess their compliance with the petrofacies, using the Multi-Resolution Graph-Based Clustering (MRGC) method. Through an analysis of pore structure type, core porosity, TOC, and brittleness index, we identified four types of lithofacies with varying reservoir quality: PF A > PF B > PF C > PF D. The compliance of the clustered electrofacies with the petrofacies obtained from the interpreted logs was found to be 85.42%. However, the compliance between the clustered electrofacies and the petrofacies obtained from the raw well logs was only 47.92%. Hence, the interpreted logs exhibit a stronger correlation with petrofacies characterization, and their utilization as input data is more beneficial in accurately predicting petrofacies through machine learning algorithms.