A machine learning-based production prediction method for offshore deep low-permeability to tight reservoirs based on reservoir classification: a case study from the western south China sea

Introduction Developing offshore deep low-permeability to tight reservoirs faces inconsistent success due to ambiguous reservoir classification and inaccurate production evaluation. This study addresses these issues by analyzing oil wells in the western South China Sea. Methods The target reservoir was classified into four types using a flow zone indicator method, and permeability conversion models were established accordingly. Using tNavigator, numerical reservoir models were constructed to evaluate the effects of permeability contrast, sedimentary rhythm, interlayers, and oil saturation on initial oil production. Key production factors were identified through Pearson analysis and grey relational analysis of test data. Production allocation was then performed for each sublayer within the test interval using the formation coefficient method. Subsequently, K-nearest neighbor (KNN) and back-propagation (BP) machine learning models were developed to predict production for different reservoir types. Results Types II and III reservoirs, distinguished mainly by effective permeability, make the dominant contribution to total well production, whereas Type IV reservoirs contribute little. Between the two machine learning approaches evaluated, the back-propagation model offered the best predictive accuracy. Discussion This study establishes a targeted prediction workflow that clarifies the contribution of individual reservoir types to total well production. It helps fill the gap in production prediction for offshore deep low-permeability to tight reservoirs and provides reliable support for oil-well stimulation design.