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The deployment of sophisticated Autonomous Inflow Control Valves (AICV) to manage reservoir uncertainty and water production in stand-alone completion is becoming increasingly popular, and the range of options available is constantly evolving. To date, much of the extensive testing performed with AICVs has assumed homogenous/dispersed flow (taking no direct account of production phase separation) and has ignored the potential role of varying screen geometries. Under a wide range of downhole conditions, stratified flow may be a more likely scenario and the full scale testing of AICV assembly under realistic downhole field conditions provides insights into the annular flow behavior and identifies critical interactions between the AICV and the screen, potentially leading to new means of enhancing AICV performance. A series of multiphase flow tests was performed on full size screen and housing assemblies to verify flow pattern under realistic conditions and assess the potential for screen geometry to have an impact on the AICV performance in stratified flow conditions. Various features of the screens, such as screen type (mesh screen, wire wrap), and screen/basepipe standoff height were investigated under various water fractions, flow rates and oil viscosities. The screen jacket was also partially blocked by a sleeve to simulate the partial burial of the screen in the wellbore. The multiphase flow patterns in the annular space around the screen and inside the valve housing were monitored through observation windows and high-speed camera, in conjunction with pressure drop across the screen and the entire assembly. Under normal flowrates, it is observed that the multiphase flow shows a stratified flow pattern around the screen, with the location of the water/oil interface highly sensitive to the oil viscosity. For high viscosity oil (100cp), the W/O interface is very low, resulting in a high water phase velocity high. This provides another reason why the onset of water production in heavy oil is often causing rapid screen plugging and high drawdown. Under these conditions, stratified flow is also prevalent in the valve housing, irrespective of the screen type. Under semi buried conditions, the screen type and standoff between the screen jacket and the basepipe played an outsize role in defining the flow pattern inside the housing. With a mesh screen and tall standoff, the flow pattern remains largely stratified while a wire wrap screen yields a bubbly/misty condition. As a result of these change in flow pattern, AICV performance is expected to be degraded when wire wrap screens are used in partially collapsed wellbore.
The deployment of sophisticated Autonomous Inflow Control Valves (AICV) to manage reservoir uncertainty and water production in stand-alone completion is becoming increasingly popular, and the range of options available is constantly evolving. To date, much of the extensive testing performed with AICVs has assumed homogenous/dispersed flow (taking no direct account of production phase separation) and has ignored the potential role of varying screen geometries. Under a wide range of downhole conditions, stratified flow may be a more likely scenario and the full scale testing of AICV assembly under realistic downhole field conditions provides insights into the annular flow behavior and identifies critical interactions between the AICV and the screen, potentially leading to new means of enhancing AICV performance. A series of multiphase flow tests was performed on full size screen and housing assemblies to verify flow pattern under realistic conditions and assess the potential for screen geometry to have an impact on the AICV performance in stratified flow conditions. Various features of the screens, such as screen type (mesh screen, wire wrap), and screen/basepipe standoff height were investigated under various water fractions, flow rates and oil viscosities. The screen jacket was also partially blocked by a sleeve to simulate the partial burial of the screen in the wellbore. The multiphase flow patterns in the annular space around the screen and inside the valve housing were monitored through observation windows and high-speed camera, in conjunction with pressure drop across the screen and the entire assembly. Under normal flowrates, it is observed that the multiphase flow shows a stratified flow pattern around the screen, with the location of the water/oil interface highly sensitive to the oil viscosity. For high viscosity oil (100cp), the W/O interface is very low, resulting in a high water phase velocity high. This provides another reason why the onset of water production in heavy oil is often causing rapid screen plugging and high drawdown. Under these conditions, stratified flow is also prevalent in the valve housing, irrespective of the screen type. Under semi buried conditions, the screen type and standoff between the screen jacket and the basepipe played an outsize role in defining the flow pattern inside the housing. With a mesh screen and tall standoff, the flow pattern remains largely stratified while a wire wrap screen yields a bubbly/misty condition. As a result of these change in flow pattern, AICV performance is expected to be degraded when wire wrap screens are used in partially collapsed wellbore.
With the ongoing increase in global energy demand, the significance of innovations in oil exploration and development technologies is rising, especially in relation to the development of unconventional reservoirs. The application of horizontal wells is becoming increasingly important in this particular situation. However, accurately monitoring and analyzing fluids in horizontal wells remains challenging due to the complex and fluctuating flow patterns of oil-water two-phase flow within the wellbore. Several elements, including well slope angle, flow rate, and water content, are involved. This study aimed to explore and develop an effective method for forecasting flow patterns, improving the precision of the dynamic monitoring of oil-water two-phase flow in horizontal wells. By analyzing the flow patterns in different experimental conditions, a predictive model using the SOA-BP neural network was developed, providing a scientific basis for dynamic monitoring in actual production scenarios. Initially, the simulated experiment for oil-water two-phase flow was carried out at room temperature and pressure utilizing a multiphase flow simulator. An optically transparent wellbore, with a diameter comparable to that of a real downhole well, was utilized, and No. 10 industrial white oil and tap water were employed as the experimental fluids. The experiment considered multiple contributing factors, including different well deviation, total flow, and water cut. The flow characteristics of oil and water were observed via visual monitoring and high-definition video, followed by detailed analysis. After collecting the experimental data, flow regimes for various scenarios were classified based on the established theory of oil-water two-phase flow in horizontal wells; then, detailed flow distribution diagrams were drawn. These data and diagrams presented offer a visual representation of the behavioral patterns exhibited by oil-water two-phase flow under varying situations and form the basis for subsequent model training and testing. Subsequently, based on the experimental data, this study combined the Seagull Optimization Algorithm (SOA) with a BP neural network to effectively learn and predict the experimental data. The SOA optimized the weights and biases of the BP neural network, improving the model’s convergence speed and prediction accuracy. Through rigorous training and testing, an oil-water two-phase flow pattern forecasting model was established, effectively predicting flow patterns under different well deviation, total flow, and water cut conditions. Finally, to validate the efficiency of the established model, a total of 15 data points were chosen from a sample well for validation. By comparing the flow patterns predicted by the model with actual logging data, the results indicate that the model’s accuracy in identifying flow pattern was 86.67%. This demonstrates that the flow pattern prediction model based on the SOA-BP neural network achieved a high level of accuracy under different complicated working conditions. This model effectively fulfills the requirements for dynamic monitoring in actual production. This indicates that the SOA-BP neural network-based flow pattern forecasting method is highly valuable due to its practical application value and provides an efficient technical approach for the development of unconventional reservoirs and the dynamic monitoring of horizontal wells in the future.
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