This paper presents a new methodology for addressing onshore oil & gas production and the constraints inherent in these networks. By translating production network models into objective functions, optimization routines can run directly on the model optimizing the entire network. Just as important, the results keep the network within operating boundaries. To scale the solution for large networks, interpolation and machine learning algorithms are employed to keep the problem from exponentially increasing in computation time.
Onshore oil and gas production typically involves dozens of wells operated by different companies. Production from these wells are transferred to midstream companies at Lease Automatic Custody Transfer (LACT) units . From the LACT units, the oil and gas are transported to processing facilities. Since multiphase flow presents a number of fluid transport issues, it is preferable or usually required that the liquid and gas be separated before being exported. Therefore, large holding tanks are a common sight on wellpads. These tanks hold the liquid until it can be carried out by tankers or pumped to an export line. If many well pads pump liquid from their holding tanks at the same time, this can cause a large pressure drop in the export line, and if the pressure exceeds the Maximum Allowable Operating Pressure (MAOP), a shutdown is triggered and regulatory action is required , which ultimately lowers the daily production from the field.
When export lines are designed, engineers typically look at average production profiles. However, when liquids are cached and then exported at once, this exceeds the operating parameters of the pipeline. Given a steady supply of oil, the designed capacity is likely far greater than the capacity in practice. That’s because of the discrete nature of the pumps filling and draining without any coordination across fields.
Expansion of onshore networks also pose a threat to pipeline capacity limits . Coupled with the inherently transient operations, midstream pipelines can become unreliable. The approach below outlines a method of improving up-time.