Abstract
In the sophisticated world of oil asset management, mainly for a large onshore asset with a complex surface network, the establishment of a calibrated surface network model is critical. The current state of the asset consists of thousands of pipelines, hundreds of tie-in connections, and several manifolds. By maintaining a daily calibration, it becomes possible to accurately pinpoint bottlenecks that hinder production, assess the hydraulic health of the pipelines, and uncover opportunities for optimization. The calibration process involves adjusting the model to match actual field data, which ensures that the model can reliably simulate the asset's performance under various conditions. The benefits of a calibrated surface network model extend beyond troubleshooting and optimization. By understanding the asset's hydraulic behavior, engineers can design interventions that preemptively address potential issues, thereby minimizing downtime and maximizing production efficiency.
The Automatic Choke Calibration (ACC) and Automatic Network Model Calibration (ANMC) workflows are critical processes for ensuring the accuracy and efficiency of wellhead and pipeline operations. The ACC workflow is a specialized procedure that calibrates wellhead chokes by selecting wells with valid Inflow Performance Relationship (IPR) and Vertical Lift Performance (VLP) files. It involves conducting eight simulation runs with varying choke discharge coefficients (CD) to match the pressure difference observed during testing. A choke is considered calibrated if the deviation is within a ±5% range. The ANMC workflow extends this calibration to the entire surface network, from the wellhead choke to the Gathering Center (GC). It identifies wells with valid IPR/VLP, calibrated chokes, valid connection length, real-time wellhead pressure (WHP), flowline pressure (FLP) and GC header pressure. The workflow utilizes averaged real-time pressure data over a 24-hour period to calibrate pipeline connections. This is achieved by adjusting the friction factor multiplier through a simulated annealing algorithm to align with observed pressure data. Successfully calibrated pipelines have their friction factor multiplier values stored in a database. The updated model provides engineers with a reliable tool for assessing virtual rates from individual wells and pipelines. Insights gained from the friction factor multiplier aid in evaluating the hydraulic health of pipelines, allowing for effective preventive maintenance. Furthermore, the calibrated model serves as a foundation for another workflow that determines necessary adjustments in choke and artificial lift parameters to optimize oil production. This systematic approach to calibration instills confidence in the reported data and supports proactive decision-making in production operations.
The paper highlights a groundbreaking approach to creating an integrated and automated workflow for daily calibration of choke and surface networks. This method contrasts with the conventional practice of less frequent calibration, which can significantly impact the daily operational recommendations for the asset. The adoption of this innovative workflow promises to enhance the accuracy and efficiency of operational decisions, leading to improved management and performance of the asset. Such advancements in workflow automation are crucial for maintaining the competitiveness and sustainability of operations in the dynamic field of asset management. The integration of real-time data and automated models, as seen in the industry, can lead to significant improvements in diagnosing issues like scale deposition in surface flow networks and optimizing pump performance.
