Stabilized and Increased Well Production Using Automatic Choke Control

Abstract

Process fluctuations are often equivalent to lost production as the necessary margins to process constraints need to take into account the fluctuations. Better operation is achieved by reduced process fluctuations so that the average production may be closer to the constraints. This is achieved by automatic process control which also reduces operator load. Wells which produce oil and free gas (coning) from the reservoir are sensitive to changes in operating conditions, and the gas and oil rates drift over time. This is natural because variations in coned free gas generate variations in wellhead pressure. This will in turn affect the production rate and the well drawdown. In manual operation, a common strategy is to operate with an excess of free gas or use artificial gas lift and manipulate the wellhead choke manually. An automatic wellhead choke control solution is developed to reduce fluctuations and increase production rates. The two main elements in the automatic wellhead choke control are: A robust measure of the controlled variable (i.e. the gas flow rate) A robust control strategy Since the gas flow rate is not directly measured, a gas flow rate estimator was developed, with the premise that no hardware modification should be necessary: The gas fraction in the well stream is determined from the pressure drop from the bottomhole to the wellhead The total multiphase well flow is calculated from either a venturi meter (if available) or the well head choke (based on choke pressure drop, travel, and characteristics) The control strategy is to ensure fast corrections to keep the gas flow rate close to the specified set point, thus enabling a close-to-collapse operation of the established gas cone. The gas flow rate set point is specified to ensure that the well head pressure is sufficiently high, i.e. higher than the pressure downstream the choke. The solution is implemented on several wells at various Statoil operated fields with gas coning into the wells. The solution gives stable well flow rates, and thus enables increased production. The operator workload related to well supervision has also been reduced. The method could also be applied to other that should remain constant, e.g. water cut, water flow, or GOR, as long as the quantity can be estimated (or measured), and will respond to well head choke manipulations.

Passive learning to address nonstationarity in virtual flow metering applications

“…The dataset of well j is {(x t,j , y t,j )} Nj t=1 , where N j is the number of observations, explanatory variables are x t,j = (u, p 1 , p 2 , T 1 , η oil , η gas ) t,j ∈ R 6 , and target variables are y t,j = Q t,j ∈ R. The η oil and η gas are the fractions of oil and gas in the fluid mixture. Ideally, the fractions should be estimated using a different model, for instance, a wellbore model as in Kittilsen et al (2014). For simplification, the fractions are approximated using the measured phasic volumetric flows.…”

Developing a Hybrid Data-Driven, Mechanistic Virtual Flow Meter -- a Case Study

“…The M-model is from Kittelsen et al (2014), chosen for its simplicity, and described with the equations ( 1)-( 7). More widespread choke models exist (e.g.…”

“…For this model, the valve flow coefficient; C v (z), is determined with linear interpolation between a given set of test points, which are usually from lab-experiments with water, yet calibrated to the multiphase flow once in place. Further nomenclature may be found in Kittelsen et al (2014). The learnable model parameters are chosen to be…”

The Choke as a Brainbox for Smart Wellhead Control

“…Furthermore, based on a generated model for measured variables (Basic Sediment and Water "BSW", Gas Liquid/Oil Ratio "GLR" and sand concentration) and set constraints, flow is adjusted. Available models are either too theoretical or require some parameters to be determined offfield [3], [4]. The method used here employs already acquired production data to generate and/or improve models.…”