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Parametric Analysis in Automation and Optimization

 Continuous and timely lift optimization on a well is dependent on the knowledge of the operational conditions of a well and its associated reservoir. When looking to maximize the economic benefit from a well at a given point in time, the operational expenditure can be inversely associated with production maximization. To generate set-point recommendations to optimize lift performance at timely intervals considering the above-mentioned setup can be a significant challenge. An example of such an optimization problem is adjusting the gas injection rate on a gas-lift well to maximize production by minimizing back-pressure on the well while accounting for gas injection cost. Popularly, the gas lift injection rate set-point is decided by an operator based on experienced estimates of well characteristics. A method to provide a physical basis to this process uses simulation models, of which the development, parametric analysis, model matching, and selection of has traditionally been a manually intensive process. This paper proposes a methodology to automate the optimization process in order to alleviate human engineering efforts and scale up to the challenges of wells-to-personnel ratios. This automated methodology includes live information ingestion, data wrangling, data clean-up, real-time generation of relevant simulations, probabilistic estimation of model relevance, gas lift set-point recommendations, response capture, adaptive updating of the model and improved recommendations over time.

The objective of gas lift optimization is maximizing the current output from a well by addressing the relationship between oil production rate and gas injection rate. The key parameter describing this relationship is the marginal increment in oil production rate per unit change in the gas lift injection rate. Under-injection of gas can lead to production rate loss due to insufficient reduction in gravitational head of production fluids. In contrast, over-injection of gas leads to high frictional head and wellhead pressure creating additional backpressure on the formation, resulting in production loss. 

Parametric analysis is a common exercise employed by production engineers in well modeling and decision making. The figure above displays a set of gas injection performance plots (Oil production rate vs. Gas injection rate) resulting from a parametric analysis of a physics-based simulator. In an example 10-day window of observation, it was observed that the oil production rate of a well varied between 70-90 barrels per day. The other uncertain parameters in the parametric analysis include the unknown reservoir or downhole conditions, and the varying surface parameter measurements. Simulations are generated for a combination of cases varying these parameters, and cases which match the operating production rates are selected.

As demonstrated in the graphic above, there is a non-unique solution resulting from the simulation. In a typical manual review, the engineer is expected to eliminate some of the possibilities to select a configuration which represents the well’s current operating state. As the figure illustrates, the gas lift performance curves can broadly be categorized into either flat or curved lines. The flat lines indicate insensitivity of the well production rate in response to changing injection rate. In such cases, the operator can save gas by reducing the injection rate without any significant impact on production. The curved lines present a possibility to improve the oil production rate by finding an optimum injection rate, and, a potential to lose production by injecting at an incorrect injection rate either by under-injecting or over-injecting.

The underlying conditions representing the individual cases are not always known to the engineer, as explained before. Hence, the major problem in gas lift optimization is model selection under uncertain and transient conditions. In our case example, the operator may make a difference of up to 20% in improvement or under-performance of the oil production rate contingent on the selection of the appropriate model. Thus, customarily, the engineer has to invest significant time to resolve the non-uniqueness of the solution and ultimately select a model based on deduction, conjecture or supposition.

To see our solutions in this automation and optimization challenge, request our white paper on the subject.