In the Jiyang Depression, sand, coal, and mudstone are interbedded, and the sand and coal layers contain high hydrocarbon content. This study addresses the optimal selection of fracturing layers for multi-gas co-production and simulates fracture propagation across interbedded coal and rock. A finite-discrete element model was established to analyze the effects of the fracturing layer, pumping rate, liquid viscosity, and cleat connections on the longitudinal propagation of hydraulic fractures in interlayer coal formations. The results show that when the fracture initiation layer is sandstone, hydraulic fractures can effectively propagate through interlayer interfaces to fracture the coal seams. When the fracture initiation layer is coal rock, hydraulic fractures in the vertical direction are confined within the coal seam and cannot propagate through the interlayer interface to fracture the sandstone layers. Hydraulic fractures are mainly connected by cleat fractures in the horizontal direction, forming complex network of fractures. Based on the research results, it is recommended that sandstone layers be used as the fracturing treatment layer for interbedded coal-bearing strata in the Jiyang Depression. The pumping rate is 22 m3/min, and the liquid viscosity is 15 mPa & centerdot;s. Through on-site production data collection, the highest daily gas production of Well A in the Jiyang Depression exceeded 20,000 m3/d, with an average daily gas production exceeding 10,000 m3/d, which significantly improved the production effect.
This paper summarizes a machine-learning competition, which is the first public benchmark comparing machine-learning (ML)-based automatic depth-shifting methods for well logs. Accurate depth determination of subsurface formations is critical for various applications in the oil and gas industry. However, achieving accurate depth registration of well logs can be challenging due to inherent ambiguity and subjectivity. Conventional depth-shifting methods relying on manual bulk shift based on peaks and troughs can be tedious and time consuming. The 2023 Machine-Learning Competition aimed to evaluate and compare various data-driven techniques for automatically aligning well logs to a reference log and correcting depth misalignments. It was organized by the Petrophysical Data-Driven Analytics (PDDA) Special Interest Group (SIG) of the Society of Petrophysicists and Well Log Analysts (SPWLA). Various machine-learning algorithms and deep-learning architectures from the top five teams were explored, including techniques like dynamic time warping (DTW), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and ridge regression, known for their ability to learn complex patterns from data. The performance of the submitted solutions was evaluated using metrics like root mean squared error (RMSE) and mean absolute deviation (MAD). The contest results, presented in this paper, highlight the effectiveness of data-driven methods in automatically rectifying well logs and provide insights into the strengths and weaknesses of different approaches for this task. The findings contribute to the advancement of automated depth-shift techniques and their potential application in subsurface characterization and reservoir management. The code and data set are available at https://github.com/ pddasig/Machine-Learning-Competition-2023. This paper only gives a high-level summary of this competition, and we refer readers to the GitHub repository for a more indepth understanding of the works.
In adjustment well cementing of old areas, underground high-pressure water layers can invade the cement slurry, leading to poor cement-stone bonding and compromised cementing quality. Previous studies had primarily focused on developing cement-slurry systems with anti-water-invasion (AWI) materials; however, the evaluation of the AWI ability of the cement slurry was often neglected. This study proposes a method to rapidly evaluate the AWI performance of cement slurries, along with an experimental approach for assessing the water invasion during coagulation. The mechanisms are investigated using Fourier transform infrared (FTIR) spectroscopy and permeable water conductivity, X-ray diffraction (XRD), and thermogravimetric (TG) analyses. The FTIR results indicate the presence of functional groups such as methylene and hydroxyl in the AWI material, which demonstrate adsorption properties, and can improve the cohesion of cement particles and thereby, the AWI ability. The results of the rapid evaluation of the AWI capability of cement slurry show a sudden increase in the conductivity of the AWI cement slurry, but at a later stage than that of the conventional cement slurry. The results of experiments conducted for assessing the water invasion during cement coagulation demonstrate that the conventional cement slurry in high-permeability cores exhibits higher water-invasion rates, wider second-interface cracks, and greater hydraulic conductivity than the AWI cement slurry in low-permeability cores under identical intrusion pressures. This indicates that combining low-permeability cores with AWI materials can effectively reduce the cement-slurry ion losses. XRD and TG analyses reveal that formation-water invasion significantly affects the conventional cement-slurry components in high-permeability cores, while showing minimal impact on AWI cement slurries in low-permeability cores. The proposed method can evaluate the AWI capability of different cement-slurry systems, laying the foundation for improving the AWI capability of adjustment well cementing slurry systems and cementing quality of adjustment wells.
Acid fracturing technology in coal seams has been proven to be an efficient coalbed methane (CBM) mining method. With the development of research, acid fracturing technology has become a hot research topic. Here, the deep No. 8 coal seam in the Daning-Jixian of the Ordos Basin is taken as the object of study. By using a series of physical simulation experiments of triaxial hydraulic fracturing and field tests, we systematically investigate the fracture propagation behavior of acid fracturing in the deep coal seam and mainly examine the acid concentration and perforating location. We discover that the direct acid fracturing effect of deep primary structural coal seam is significant, and the fracturing ability is stronger than that of shallow broken and soft coal seams. During the acid fracturing process, there is no large filtration phenomenon, which makes it easy to form complex, long fractures. With the increase of acid concentration in the same perforated location, the complex acid fracturing fractures are more likely to form. The field test shows that 10% sulfamic acid concentration is preferred, with the consideration of fracture complexity, fracture height control, fracture pressure, and construction safety. Moreover, we observe that the perforation location is set at the top of the coal seam, which is conducive to the formation of a complex seam network. The work provides a guiding significance for the design of acid fracturing in deep coal reservoirs.
Groundwater significantly impacts the stability of rock engineering and mineral resource exploitation. Mechanical parameters and fracture characteristics of rocks are crucial in theoretical analysis, experimental testing, and numerical simulation. To investigate the weakening mechanism and fracture energy characteristics of water-saturated rocks, sandstone specimens with varying water-bearing states were prepared through a drying and soaking process, and uniaxial compression acoustic emission (AE) experiments were conducted. The results show that with the increase of soaking duration, the compressive strength and elastic modulus exhibit a negative exponential decreasing trend and eventually tend to stabilize. Based on the theory of energy accelerated release, the characteristics of the AE energy release rate are quantitatively analyzed. The AE energy of dry specimens is more likely to be released in the form of accelerated, abrupt changes, while that of water-bearing specimens is more likely to be released in a slow and gradual manner. During the loading process, the values of AE energy follow a normal distribution. The weakening of the internal structure of rocks and the enhanced attenuation of wave propagation result in the AE energy expectation of water-bearing specimens being significantly lower than that of dry specimens. Moreover, the upward trend of the expectation for dry specimens near failure is more significant than that of water-bearing specimens. This study concludes that water soaking softens mineral particles and weakens microstructure, resulting in the degradation of the mechanical parameters and the increase of the attenuation. The reduced and gradual AE energy release in saturated specimens reflects a transition from sudden brittle failure to progressive plastic failure in the rock.
