Experimental Investigation on the Crack Evolution of Marine Shale with Different Soaking Fluids

Hydration induced cracks could promote the complexity of hydraulic fractures in marine shale gas reservoir. But the evolution process and forming mechanism has not been fully investigated. In this paper, Longmaxi marine shale were collected and immersed in three types of fluids (distilled water, fracturing fluid, and mineral oil) for more than 10 days. The spatial-temporal evolution of soaking fractures was recorded and analyzed. A fracture mechanical model was established, considering the effects of in-situ stress, fluid pressure, hydration stress, and capillary force. The promotion mechanism of hydration cracks in forming complex fracking network was discussed. Results showed that hydration fractures were extremely developed and evenly distributed in a state of network for specimens immersed in distilled water. For specimens soaked in fracturing fluid, the hydration cracks were moderately developed for the addition of anti-swelling agent. Fractures were rarely developed for specimens treated in mineral oil. The hydration fractures were mainly formed in the first 5 h and showed strong anisotropy. Cracks parallel to the bedding planes accounted for the vast majority, with a small proportion developed in vertical direction. Theoretical calculations indicated that the stress intensity factor (SIF) caused by hydration stress and capillary force was greater than the measured fracture toughness. The micro crack would probably propagate along bedding planes and grow up into macro horizontal fractures, which promoted the formation of crisscrossing fracture network in shale gas formation.

Clarifying the Effect of Clay Minerals on Methane Adsorption Capacity of Marine Shales in Sichuan Basin, China

The effect of clay minerals on the methane adsorption capacity of shales is a basic issue that needs to be clarified and is of great significance for understanding the adsorption characteristics and mechanisms of shale gas. In this study, a variety of experimental methods, including XRD, LTNA, HPMA experiments, were conducted on 82 marine shale samples from the Wufeng–Longmaxi Formation of 10 evaluation wells in the southern Sichuan Basin of China. The controlling factors of adsorption capacities were determined through a correlation analysis with pore characteristics and mineral composition. In terms of mineral composition, organic matter (OM) is the most key methane adsorbent in marine shale, and clay minerals have little effect on methane adsorption. The ultra-low adsorption capacity of illite and chlorite and the hydrophilicity and water absorption ability of clay minerals are the main reasons for their limited effect on gas adsorption in marine shales. From the perspective of the pore structure, the micropore and mesopore specific surface areas (SSAs) control the methane adsorption capacity of marine shales, which are mainly provided by OM. Clay minerals have no relationship with SSAs, regardless of mesopores or micropores. In the competitive adsorption process of OM and clay minerals, OM has an absolute advantage. Clay minerals become carriers for water absorption, due to their interlayer polarity and water wettability. Based on the analysis of a large number of experimental datasets, this study clarified the key problem of whether clay minerals in marine shales control methane adsorption.

First Success of Marine Shale Gas in Ordos Basin: A Review of Recent Exploration Breakthrough in Ordovician Wulalike Formation

Ordos basin in central China is well known for its rich accumulation of natural resources, including Triassic tight oil and Permian tight gas. A recent exploration breakthrough shows that Ordovician shale in the same basin is also promising. The purpose of this study is to capture the engineering details of two horizontal exploration wells exploration in Wulalike formation, which mark the first production of marine shale gas in Ordos basin. The Ordovician Wulalike formation in the Ordos basin was previously seen as source rock. During early exploration in the 2010s, the formation was found to be gas bearing. However, the Wulalike shale formation shows very different features compared to the Triassic lacustrine shale in the same basin and the Silurian marine shale from Sichuan. The abundance of natural fissures, the low reservoir pressure, and the tendency to produce water are unique challenges and concerns for the Wulalike shale formation. Based on the pilot well evaluations, two horizontal wells were drilled and completed in the Wulalike formation in different locations in the western Ordos basin in 2019–2020. Both wells were well-landed in the target zone and were completed with multistage large-scale fracturing treatments. Following the well completions, flowback and production tests lasted for 3 to 5 months. Production tests showed that well 1 reached an economically acceptable gas rate in natural flow for a long-term period, producing 20,000 to 60,000 std m3/d, and well 2 produced good gas in the early period but was soon overwhelmed by massive water production. Both wells were evaluated with production logging tools. In well 1, fiber-optic distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) were used, and in well 2, a production logging tool (PLT) was used. The positive gas production from both wells marks the first production of marine shale gas in the Ordos basin. The understanding of the geology and reservoir, the use of unconventional fracturing and completion practices, the assistance of energized fluid, and post-treatment artificial lift are the technologies that helped achieve this success. Further study is needed on the complexity of the natural fissures to lower the risk of unwanted water production from the Wulalike rocks. The first successful production from the Wulalike is very critical for the exploration of the Ordovician section in the Ordos basin because it helps to confirm a favorable exploration and appraisal area of 2000 to 3000 km2, which has the potential to turn into a huge reserve. This case study provides value from a technical standpoint, as very few success stories have been reported from low-pressure shale gas previously in China or worldwide.

Utilizing Data-Driven Models to Predict Brittleness in Tuscaloosa Marine Shale: A Machine Learning Approach

The Tuscaloosa Marine Shale (TMS) formation is a clay- and liquid-rich emerging shale play across central Louisiana and southwest Mississippi with recoverable resources of 1.5 billion barrels of oil and 4.6 trillion cubic feet of gas. The formation poses numerous challenges due to its high average clay content (50 wt%) and rapidly changing mineralogy, making the selection of fracturing candidates a difficult task. While brittleness plays an important role in screening potential intervals for hydraulic fracturing, typical brittleness estimation methods require the use of geomechanical and mineralogical properties from costly laboratory tests. Machine Learning (ML) can be employed to generate synthetic brittleness logs and therefore, may serve as an inexpensive and fast alternative to the current techniques. In this paper, we propose the use of machine learning to predict the brittleness index of Tuscaloosa Marine Shale from conventional well logs. We trained ML models on a dataset containing conventional and brittleness index logs from 8 wells. The latter were estimated either from geomechanical logs or log-derived mineralogy. Moreover, to ensure mechanical data reliability, dynamic-to-static conversion ratios were applied to Young's modulus and Poisson's ratio. The predictor features included neutron porosity, density and compressional slowness logs to account for the petrophysical and mineralogical character of TMS. The brittleness index was predicted using algorithms such as Linear, Ridge and Lasso Regression, K-Nearest Neighbors, Support Vector Machine (SVM), Decision Tree, Random Forest, AdaBoost and Gradient Boosting. Models were shortlisted based on the Root Mean Square Error (RMSE) value and fine-tuned using the Grid Search method with a specific set of hyperparameters for each model. Overall, Gradient Boosting and Random Forest outperformed other algorithms and showed an average error reduction of 5 %, a normalized RMSE of 0.06 and a R-squared value of 0.89. The Gradient Boosting was chosen to evaluate the test set and successfully predicted the brittleness index with a normalized RMSE of 0.07 and R-squared value of 0.83. This paper presents the practical use of machine learning to evaluate brittleness in a cost and time effective manner and can further provide valuable insights into the optimization of completion in TMS. The proposed ML model can be used as a tool for initial screening of fracturing candidates and selection of fracturing intervals in other clay-rich and heterogeneous shale formations.