Abstract
Gas hydrate problems are one of the major challenges in offshore and deepwater operations. One common method for avoiding hydrate problems is to inject thermodynamic hydrate inhibitors at upstream (e.g., methanol (MeOH), mono ethylene glycol (MEG) and ethanol), whilst low dosage hydrate inhibitors including anti-agglomerates (AAs) and kinetic hydrate inhibitors (KHIs) are increasingly used in recent years. Ethanol is the most popular inhibitor used in Brazil. Hydrate inhibitors are injected based on the predicted hydrate phase boundary and the operating conditions, taking into account the amount of inhibitor loss to hydrocarbon phases and a safety margin. Generally, there is no means to monitor the actual concentration of inhibitors in downstream.
A new technique has been developed recently for optimizing hydrate inhibitor injection rates by monitoring the actual hydrate inhibitor concentrations downstream. The technique determines the salt and inhibitor (alcohols, KHIs, and AAs) concentrations by measuring speed of sound and electrical conductivity in an aqueous sample taken from pipeline/separator.
In this communication, we report the further development of this technique for ethanol-salt system, following the success for MeOH-salt systems, MEG-salt systems, and several different KHIs-salt systems. An artificial neural network correlation has been developed for aqueous sample solutions containing ethanol and salts. This correlation covers a salt concentration from 0 to 10 mass% and ethanol concentration from 0 to 50 mass% in aqueous solution. The results demonstrate that this technique can be used to determine salt and ethanol concentrations in the presence of salts for optimizing inhibitor injection rates, hence reducing the operational costs and impact on the environment. This technique can also improve the reliability of production systems against unexpected events like pump malfunction and/or changes in production variables (e.g., water cut) and/or human error.
A new technique has been developed recently for optimizing hydrate inhibitor injection rates by monitoring the actual hydrate inhibitor concentrations downstream. The technique determines the salt and inhibitor (alcohols, KHIs, and AAs) concentrations by measuring speed of sound and electrical conductivity in an aqueous sample taken from pipeline/separator.
In this communication, we report the further development of this technique for ethanol-salt system, following the success for MeOH-salt systems, MEG-salt systems, and several different KHIs-salt systems. An artificial neural network correlation has been developed for aqueous sample solutions containing ethanol and salts. This correlation covers a salt concentration from 0 to 10 mass% and ethanol concentration from 0 to 50 mass% in aqueous solution. The results demonstrate that this technique can be used to determine salt and ethanol concentrations in the presence of salts for optimizing inhibitor injection rates, hence reducing the operational costs and impact on the environment. This technique can also improve the reliability of production systems against unexpected events like pump malfunction and/or changes in production variables (e.g., water cut) and/or human error.
| Original language | English |
|---|---|
| Pages | 1-8 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - Oct 2011 |
| Event | Offshore Technology Conference - Rio de Janeiro, Brazil Duration: 4 Oct 2011 → 6 Oct 2011 |
Conference
| Conference | Offshore Technology Conference |
|---|---|
| Country/Territory | Brazil |
| City | Rio de Janeiro |
| Period | 4/10/11 → 6/10/11 |