A Static Data-Driven Approach for Predicting the Dynamic Distribution of Physical Fields in Coalbed Methane Reservoirs with Multi-Fractured Horizontal Well

Accurately predicting the dynamic distribution of physical fields (including the gas saturation field, fracture pressure field, and matrix pressure field) in coalbed methane (CBM) reservoirs not only enables monitoring reservoir changes, but also provides support for refined reservoir management, which is of great significance for achieving efficient development and sustainable utilization of CBM reservoirs. However, existing research mainly focuses on CBM production prediction or pore pressure change analysis, and there are few studies on the prediction of the dynamic distribution of physical fields in CBM reservoirs. The systematic prediction of the dynamic distribution of physical fields is still in the exploratory stage. To address this research gap, this paper introduces a deep learning approach that utilizes static data to predict the dynamic distribution of physical fields. This method takes reservoir porosity, permeability, coal seam thickness, adsorption and desorption parameters, reservoir pressure, and other static data (collectively referred to as reservoir geological parameters), as well as multi-stage fracture information as input, and builds the pretraining model for multi-source feature fusion (MSFF-PM) and the multi-scale integrated convolutional long short-term memory (ConvLSTM) model (MSI-ConvLSTM). During inference, the framework predicts the dynamic distribution of physical fields from static reservoir parameters and fracture information without requiring historical field maps as external inputs. The coefficients of determination (R2) for gas saturation, fracture pressure, and matrix pressure predictions over the first five years were 0.953, 0.944, and 0.946, respectively. These findings are limited to the evaluated synthetic cases with homogenized background properties and idealized planar bi-wing fractures. These results demonstrate agreement with the CMG GEM reference fields within the evaluated synthetic cases.