Deep insight: an efficient hybrid model for oil well production forecasting using spatio-temporal convolutional networks and Kolmogorov-Arnold networks.

As global energy demand grows, the oil and gas industry faces increasing challenges in optimizing production while achieving sustainability. Accurate oil well production forecasting is essential for effective resource management and operational decision-making. However, traditional mathematical models struggle with the nonlinear and dynamic characteristics of production data, while existing hybrid neural networks often lack sensitivity to operational changes and suffer from overcomplexity due to numerous parameters.

Research on the Application of Integrated Learning Models in Oilfield Production Forecasting.

Forecasting oil production is crucially important in oilfield management. Currently, multifeature-based modeling methods are widely used, but such modeling methods are not universally applicable due to the different actual conditions of oilfields in different places. In this paper, a time series forecasting method based on an integrated learning model is proposed, which combines the advantages of linearity and nonlinearity and is only concerned with the internal characteristics of the production curve itself, without considering other factors.

Optimization of Gel Flooding during the High Water Cut Stage in a Conglomerate Reservoir of the Xinjiang A Oilfield.

Influenced by water injection, a dominant flow channel is easily formed in the high water cut stage of a conglomerate reservoir, resulting in the inefficient or ineffective circulation of the injected water. With gel flooding as one of the effective development methods to solve the above problems, its parameter optimization determines its final development effect, which still faces great challenges. A new optimization method for gel flooding is proposed in this paper.

Polymer Flooding in Heterogeneous Heavy Oil Reservoirs: Experimental and Simulation Studies.

Polymer flooding (PF) in heterogeneous heavy oil reservoirs is not only closely related to polymer degradation, but also to non-Newtonian flow. In this paper, both experimental and simulation methods are combined to investigate this type of flooding. Through experiments, the degradation of polymer, rheological properties of fluids, and flow of fluids in porous media were determined.

Effect of Non-Newtonian Flow on Polymer Flooding in Heavy Oil Reservoirs.

The flow of polymer solution and heavy oil in porous media is critical for polymer flooding in heavy oil reservoirs because it significantly determines the polymer enhanced oil recovery (EOR) and polymer flooding efficiency in heavy oil reservoirs. In this paper, physical experiments and numerical simulations were both applied to investigate the flow of partially hydrolyzed polyacrylamide (HPAM) solution and heavy oil, and their effects on polymer flooding in heavy oil reservoirs. First, physical experiments determined the rheology of the polymer solution and heavy oil and their flow in porous media.

Effect of Polymer Degradation on Polymer Flooding in Heterogeneous Reservoirs.

Polymer degradation is critical for polymer flooding because it can significantly influence the viscosity of a polymer solution, which is a dominant property for polymer enhanced oil recovery (EOR). In this work, physical experiments and numerical simulations were both used to study partially hydrolyzed polyacrylamide (HPAM) degradation and its effect on polymer flooding in heterogeneous reservoirs. First, physical experiments were conducted to determine basic physicochemical properties of the polymer, including viscosity and degradation.