Characteristics and Genetic Mechanism of Chang Eight Low Permeability and Tight Reservoir of Triassic Yanchang Formation in Central-East Ordos Basin

In this article, the characteristics of Chang 8 reservoir of Triassic Yanchang Formation in northern Shaanxi are studied by using polarizing microscope, field emission scanning electron microscope, image particle size, X-ray diffraction analysis of clay, and constant pressure mercury intrusion. The study shows that the target layer is in a relatively stable and uniform sinking burial period after deposition, and the lithology composition in the area is relatively complex, mainly composed of debris–feldspar sandstone and feldspar sandstone, with the characteristics of fine grain and high content of interstitial material. The porosity of the reservoir is generally between 4% and 12%, with an average of 8.05%. The permeability is generally between 0.03 × 10−3 and 0.5 × 10−3 μm2, with an average of 0.16 × 10−3 μm2. Strong compaction and well-developed cementation of calcareous, siliceous, and authigenic illite are important reasons for the formation of extra-low porosity and extra-low permeability reservoirs. But at the same time, because of the protective effect of chlorite film, some residual intergranular pores are preserved, which makes the some reservoirs with relatively good physical property, forming a local relatively high-porosity and high-permeability section of the “highway.”

Semi-Supervised Learning–Based Petrophysical Facies Division and “Sweet Spot” Identification of Low-Permeability Sandstone Reservoir

The identification of the “sweet spot” of low-permeability sandstone reservoirs is a basic research topic in the exploration and development of oil and gas fields. Lithology identification, reservoir classification based on the pore structure and physical properties, and petrophysical facies classification are common methods for low-permeability reservoir classification, but their classification effect needs to be improved. The low-permeability reservoir is characterized by low rock physical properties, small porosity and permeability distribution range, and strong heterogeneity between layers. The seepage capacity and productivity of the reservoir vary considerably. Moreover, the logging response characteristics and resistivity value are similar for low-permeability reservoirs. In addition to physical properties and oil bearing, they are also affected by factors such as complex lithology, pore structure, and other factors, making it difficult for division of reservoir petrophysical facies and “sweet spot” identification. In this study, the logging values between low-porosity and -permeability reservoirs in the Paleozoic Es3 reservoir in the M field of the Bohai Sea, and between natural gamma rays and triple porosity reservoirs are similar. Resistivity is strongly influenced by physical properties, oil content, pore structure, and clay content, and the productivity difference is obvious. In order to improve the identification accuracy of “sweet spot,” a semi-supervised learning model for petrophysical facies division is proposed. The influence of lithology and physical properties on resistivity was removed by using an artificial neural network to predict resistivity R0 saturated with pure water. Based on the logging data, the automatic clustering MRGC algorithm was used to optimize the sensitive parameters and divide the logging facies to establish the unsupervised clustering model. Then using the divided results of mercury injection data, core cast thin layers, and logging faces, the characteristics of diagenetic types, pore structure, and logging response were integrated to identify rock petrophysical facies and establish a supervised identification model. A semi-supervised learning model based on the combination of “unsupervised supervised” was extended to the whole region training prediction for “sweet spot” identification, and the prediction results of the model were in good agreement with the actual results.

4D Geomechanical Research for Fracturing Optimization of Low-Permeability Reservoir

China has abundant low-permeability oil and gas resources. A lot of practice has proved that low-permeability reservoirs must undergo hydraulic fracturing to obtain commercial production capacity. Geomechanical characteristics are the key factor for fracturing. It plays a very important role in the oil field exploration and production. It is not only the driving force for oil and gas migration, but also provides a basis for wellbore stability analysis and drilling optimization design. The state of the formation stress field and the mechanical properties of the rock jointly determine the direction, shape and orientation of the fracture extension of the fracturing. Together it affects the stimulation effect of fracturing. Realizing the high-efficiency development of low-permeability reservoirs is a key and difficult problem facing for oil filed operator. Horizontal wells drilling and hydraulic fracturing are the core technology for increasing single well production in low-permeability reservoirs. The effectiveness of reservoir reconstruction directly determines the production capacity of the reservoir. In order to clarify the influence of static and dynamic geomechanics on the scale of reservoir stimulation in the process of horizontal well fracturing, and ultimately provide effective technical support for the formulation and optimization of reservoir stimulation design. This study is based on the study of single well one-dimensional geomechanics, using the structural characteristics and seismic attributes of low-permeability reservoirs to study the characteristics of the three-dimensional spatial distribution of mechanics. On this basis, combined with real-time fracturing treatment data and micro-seismic monitoring data, dynamic (four-dimensional) stress field simulations are continuously carried out. The research results can be mainly used to guide the optimization of reservoir stimulation and the evaluation of filed development plan.

Study on the Influential Factors of CO2 Storage in Low Permeability Reservoir

As the demands of tight-oil Enhanced Oil Recovery (EOR) and the controlling of anthropogenic carbon emission have become global challenges, Carbon Capture Utilization and Sequestration (CCUS) has been recognized as an effective solution to resolve both needs. However, the influential factors of carbon dioxide (CO2) geological storage in low permeability reservoirs have not been fully studied. Based on core samples from the Huang-3 area of the Ordos Basin, the feasibility and influential factors of geological CO2 sequestration in the Huang-3 area are analyzed through caprock breakthrough tests and a CO2 storage factor experiment. The results indicate that capillary trapping is the key mechanism of the sealing effect by the caprock. With the increase of caprock permeability, the breakthrough pressure and pressure difference decreased rapidly. A good exponential relationship between caprock breakthrough pressure and permeability can be summarized. The minimum breakthrough pressure of CO2 in the caprock of the Huang-3 area is 22 MPa, and the breakthrough pressure gradient is greater than 100 MPa/m. Huang-3 area is suitable for the geological sequestration of CO2, and the risk of CO2 breakthrough in the caprock is small. At the same storage percentage, the recovery factor of crude oil in larger permeability core is higher, and the storage percentage decreases with the increase of recovery factor. It turned out that a low permeability reservoir is easier to store CO2, and the storage percentage of carbon dioxide in the miscible phase is greater than that in the immiscible phase. This study can provide empirical reference for caprock selection and safety evaluation of CO2 geological storage in low permeability reservoirs within Ordos Basin.