Production optimization

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Product optimization - Wikipedia

In petroleum fields, hydrocarbon production is often constrained by reservoir conditions, deliverability of the pipeline network, fluid handling capacity of surface facilities, safety and economic considerations, or a combination of these considerations. While production can be controlled by adjusting well production rates, allocating lift-gas rates, and in some fields, by switching well connections from one flowline to another flowline, implementation of these controls in an optimal manner is not easy.

The objective of dynamic production optimization is to find the best operational settings at a given time, subject to all constraints, to achieve certain operational goals. These goals can vary from field to field and with time.

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Typically one may wish to maximize daily oil rates or minimize production costs. Various aspects of production optimization have been addressed in the literature. For example, several researchers have studied the problem of allocating limited amount of available gas to specified wells for continuous gas-lift. Fang and Lo 6 proposed a linear programming technique to allocate lift-gas and well rates subject to multiple flow rate constraints. Barnes et al.

How to use machine learning for production optimization

This model maximizes oil production while minimizing the need for gas processing. Several papers have reported results for the production optimization of the Kuparuk River field in Alaska. The techniques published so far either addressed only a part of the optimization problem of interest to us or made significant simplifications during the optimization process.

In most commercial reservoir simulators 11,12 , flow rate constraints on facilities are handled sequentially by ad hoc rules. In addition, gas-lift optimization is done separately from the allocation of well rates. Because of the nonlinear nature of the optimization problem and complex interactions, results from such procedures can be unsatisfactory. In a companion paper Wang et al.

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While this approach was successful, it was limited in its ability to handle flow interactions among wells when allocating well rates and lift-gas rates. Here we extend the work of Wang et al.

The optimization problem is solved by a sequential quadratic programming algorithm, which is a derivative-based nonlinear optimization algorithm. The proposed method is tested on several examples. Results show that the method is capable of handling flow interactions among wells and can be applied to a variety of problems of varying complexities and sizes.

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What is Production Optimisation - Production - CA CPT - CS & CMA Foundation - Class 11 - Class 12

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