Summary: This study analyzes the production behaviors of six deep coalbed-methane (CBM) wells (>1980 m) completed in the Ordos Basin and presents a machine-learning method to predict gas production for six target wells. The production behaviors of target wells are characterized with several months of rapidly declining pressure, following by several years of stabilized gas rate and pressure. Production data analysis suggests a relatively large amount of free gas (but limited free water) in coal seams under in-situ condition. The production mechanisms generally transit from free-gas expansion and fracture/cleat closure at early stage to gas desorption at later stage. We treated the target wells’ production data as time-series data and applied the Long Short-Term Memory (LSTM) model on the target wells for gas-rate predictions. We also employed a Bayesian-probabilistic method to optimize the LSTM model (BO-LSTM). Our results demonstrate the BO-LSTM model’s robustness in gas-rate predictions for target wells. Also, treating casing pressure and liquid level as inputs is sufficient for the BO-LSTM model to reach a reliable production forecast. This study provides a promising tool to forecast the gas production of deep-CBM wells using surface rates and pressure data. The findings of this study may guide the reservoir management and development-strategy optimizations of deep-CBM reservoirs.

Studies on stress sensitivity of relative permeability in the laboratory were mostly carried out by varying overburden pressure at constant pore pressure (PP). However, in a real oilfield situation, changes in net stress are induced by changes in PP rather than overburden pressure. This research presents the effect of PP variation at a confining stress of 25 MPa on water–oil and silica nanofluid–oil relative permeability curves. Results showed that, at low PP variations of 0.1, 2, and 5 MPa, two-phase flow parameters exhibited distinct trends. Thus, for water–oil relative permeability, initial water (S_wi) and residual oil (S_or) saturations both decreased as PP increased. End-point oil K_ro (S_wi) and water K_rw (S_or) relative permeabilities both increased for water flooding. Similar trends were observed for nanofluid flooding. End-point mobility ratio and displacement efficiency both increased with increase in PP within this range for water and nanofluid flooding. At 10 MPa PP, S_wi decreased compared to low PP variations, while S_or increased significantly. Similar observation was made for nanofluid flooding except for S_or which had moderate increment. End-point mobility ratio increased significantly for water flooding, but had a slight increase for nanofluid flooding compared to low PP variations. Displacement efficiency decreased for both water and nanofluid flooding compared to low PP variations. At the same PP, water–oil and nanofluid–oil relative permeabilities showed similar S_wi and K_ro (S_wi) but differing S_or and K_rw (S_or). The findings provide insights into the effect PP variation on relative permeability and would be important in flooding design considerations.

Coalbed methane (CBM) is an important unconventional energy resource, and its micropore structure has a vital impact on its exploitation. Based on the nuclear magnetic resonance (NMR) experiment, the low-temperature liquid nitrogen adsorption experiment, and the contact angle experiment, in this paper, we investigated the influence of nanofluids on the micropore structure of a CBM reservoir from many aspects. The influence of different adsorption mechanisms of TiO₂ nanoparticles on the surface wettability of rock samples was analyzed. The influence of nanoparticle adsorption on the drainage and distribution of liquid in the rock sample was discussed in depth. In addition, the effects of nanofluid treatment on the micropore structure were investigated by comparing the data of low-temperature liquid nitrogen adsorption experiments, including the pore diameter, pore volume, and specific surface area (SSA). The experimental results show that the treatment of nanofluids helps to open the micropores and greatly increases the SSA, pore diameter, and pore volume of the sample. The maximum increase percentages of SSA, pore volume, and pore diameter are 228.12, 80.65, and 18.89%, respectively. It is found that the adsorption of particles is conducive to enhancing the water wettability of the pore throat surface and reducing the damage to water locks.