Probabilistic assessment of shale gas production and water demand at Xiuwu Basin in China

Abstract

This study presents an integrated probabilistic framework by combining Monte Carlo Simulation with a gas transport model of a horizontal well with multi-fracturing stages to assess shale gas resources in the Wangyinpu Formation of the Xiuwu Basin, China. Modeling results suggest that the 30-year cumulative production of a single horizontal well is predicted at a likely value of 3.50×10 8 m 3 with a maximum of 6.78×10 9 m 3. Potential shale gas production from a "sweet spot" area is estimated at a range of 1.13×10 10 m 3 to 1.76×10 13 m 3 with a likely value of 8.24×10 11 m 3. Sensitivity analysis indicates that the gas production rate and cumulative gas production of a single horizontal well are most sensitive to the relative volume occupied by kerogen in the bulk volume of the shale, gas desorption rate, number of fracturing stages, and permeability of the stimulated zone. Assessment of water demand for horizontal well drilling and hydraulic fracturing suggests that shale gas development at the Xiuwu Basin will not likely cause regional water-supply stress because of abundant water resources in the region. The probabilistic approach presented in this study can provide valuable information for planning shale gas development and can also be applied to other shale gas reservoirs.

Parameter determination for a numerical approach to undeveloped shale gas production estimation: The UK Bowland shale region application

“…The deterministic approach presents three scenarios while the hybrid (Both Probabilistic and deterministic) provides an alternative production prospect. Sensitivity analysis is essential in shale gas production modelling due to the uncertainty associated with parameters (Zou et al, 2016). The conducted sensitivity analysis establishes the impact of the input parameters on production rate and ultimate recovery.…”

“…Nevertheless, these gains and characteristics all aim to increase the area of fracture. In our model the area of fracture (REI) is conservatively assumed as 1% of a 7400ft 2 lateral length, Zou et al, (2016) applies a minimum value of 6903m 2 equivalent to about 74,303ft 2 . Further research and clarification is needed to reduce uncertainty in the well specific rock exposure index parameter.…”

“…The early phase of shale gas development is uncertain and risky due to limited knowledge of the EUR (Weijermars 2013). Zou et al, (2016) highlights shale gas resource assessment challenges to be typified by large uncertainties as well as complex geological, petrophysical and geochemical factors. Yuan et al, (2015), based on statistics and analyses, states that there only few studies available that address input parameter prediction *UCL Energy Institute, University College London, Central House, 14 Upper Woburn Place London; 1 Corresponding author, Email: g.anandarajah@ucl.ac.uk issues for shale gas development.…”

Hybrid Machine Learning-Mathematical Programming Approach for Optimizing Gas Production and Water Management in Shale Gas Fields

“…Modeling techniques have been used to predict such variabilities . Hybrid methods have been reported, where Monte Carlo simulation, autoregressive integrated moving average (ARIMA) model, different index analogy methods, polynomial models, and response surface approach have been evaluated for predicting shale gas production. − On the other hand, machine learning is an alternative widely used in various study areas due to its high accuracy in the development of highly complex models, , so some machine learning models have been reported to make predictions in shale gas wells given information from real wells. Wang and Chen developed a comprehensive data mining process to evaluate the production performance of shale gas wells in the Montney formation by comparing four machine learning techniques (random forest (RF), adaptive boosting (AdaBoost), support vector machine (SVM), and artificial neural network (ANN)).…”

Analysis of Enhanced Oil Recovery Methods Like Gas Injection in the Fractured Reservoirs and Optimize Its Efficiency

“…Oil expansion is a very important part among those mechanisms if without availability of other artificial introduced energy. The rock and fluids expand due to their individual compressibility [1][2][3][4]. Since the fluid was expanded and the matrix pore volume was imbibed by the surrounding fluid, the reservoir pressure was plunged.…”