Offshore Production in the Gulf of Thailand

Oil and gas producers in the Gulf of Thailand face unique technical challenges. This review of papers illustrates some of the innovative solutions used in the region.


This review of technical challenges facing oil and gas producers in the Gulf of Thailand arose from last month’s meeting in Bangkok, Thailand, of the SPE Board of Directors with the SPE Asia Pacific Advisory Council, which is represented by senior executives from across the Asia Pacific region and industry value chain. It was an opportunity for Board members to meet with the leadership of the major oil and service companies and discuss how best the SPE can serve its membership in the region.

The SPE Board of Directors meets three times per year. One meeting is held in conjunction with the SPE Annual Technical Conference and Exhibition (ATCE), usually during September or October; the other meetings are held in locations around the world chosen for strategic reasons by the SPE President.

The Gulf of Thailand

Gulf of Thailand

Thailand is an oil and natural gas producer. However, the country increasingly relies on hydrocarbon imports to sustain its rising fuel demand. Most of the country’s crude oil and condensates are from offshore fields in the Gulf of Thailand. The Gulf of Thailand is bordered by Thailand, Cambodia, and the southwestern edge of Vietnam.

The state-run PTT Exploration and Production (PTTEP) and Chevron operate several of the country’s largest producing crude oil fields (EIA 2017). Several independent companies operate shallow-water crude oil and gas fields.

As output in mature fields declines, operators are trying to extend the field life and maximize the full potential of the asset. During the past few years, many SPE papers were presented by companies operating in the Gulf that address efforts being made to adopt technology to the local environment and address operational challenges.

Minimum Facility Platform

PTTEP operates two major gas fields: Bongkot and Arthit. They are located in 80-m water depth in the Gulf. The fields were originally developed using conventional wellhead platforms (CWP).

In order to reduce investment cost and make the development of marginal reservoirs more economical, PTTEP developed the concepts of medium facility platform (MFP) and small facilities platform (SFP) (Phanichtraiphop 2016). The aim is to reduce production costs while meeting the same operational functions as the conventional wellhead platforms.

Some of the key features of the MFP include: reducing the number of well slots from 16 to 9–15; using comingled flowlines; integrating booster compressor manifold with test manifold; and relocatable design for offshore pedestal crane and pig launcher.

Compared to MFP, the SFP platform has six wells instead of 6–15, tripod jacket instead of four-legged jacket, and common overpressure protection at the manifold instead of overpressure protection at the individual flowlines. The design flow rate is 40 MMscf/D, 1,400 BOPD, and 2,000 BWPD.

By standardizing platforms design to three different sizes, PTTEP can achieve flexibility in field development by matching suitable platform design with reservoir requirements.

The authors concluded that this “fit for purpose design” represents the future of field development of wellhead platforms in the Gulf of Thailand.

PTTEP’s three generations of wellhead platforms (IPTC 18786).

Sand Production Management

Managing sand production from loosely consolidated formations is critical to prevent erosion problems in the chokes, valves and piping, as well as downhole restrictions to flow. Typical sand control completions include gravel packs and screens. However, such completions are designed to handle a specific range of particle sizes, and are not effective when the particle size distribution from the producing formation is wide.

The Abu Dhabi-based Mubadala Petroleum is the operator of the Nong Yao field in the southern Gulf of Thailand (Chigbo et al. 2016), located 145 km off the coast. The field is made up of shallow clastic reservoirs. Due to the shallow nature of the reservoirs, the sands are largely unconsolidated.

Analyses of samples from the Nong Yao sands indicate the particle size distributions vary significantly across the field. Screen retention testing was conducted using several conventional screens, with none of the tests yielding positive results. Further complicating the sand production problem is that the wells are required to be artificially lifted with electrical submersible pumps (ESP), increasing the drawdown across the sandface.

To overcome this challenge, a special multimedia mesh screen was tested. It consists of base pipe wrapped with an approximately 6-mm thick layer of compressed steel wool and protected by a perforated shroud. The stainless steel fibers criss-cross to create a screen with a wide and random distribution (15–600 microns) of angular-shaped pores, making it insensitive to particle size distribution and less susceptible to plugging. The screens maintain a large open flow area (40%) and are robust and corrosion-resistant.

The specially designed screen was installed on 14 wells across the field. After more than 1 year of operation, no sand problems were observed, even in several high-water-cut wells being produced at high ESP frequencies. Pressure analyses of the wells indicate low drawdown pressures across the sandface and low skin values, suggesting that there are no issues with plugging in the completions. The deployment of this sand control technique is a first in the Gulf of Thailand, and was successful in meeting the operational challenges to achieve the required field production performance.

Optimizing ICD Design

Chevron Thailand E&P operates a number of fields located in the Gulf of Thailand and has drilled 120 horizontal wells since 1999 (Van der Bol et al. 2016). Since 2011, a total of 30 wells were successfully completed with inflow control device (ICD) completions.

In order to optimize the ICD design of each well, an innovative modeling workflow is applied. Rather than using traditional nodal-based analytical well models for the ICD design, a 3D dynamic reservoir simulator is used to conduct single-well modeling to screen candidate wells and optimize the design while drilling.

Prior to drilling the well, an initial model is constructed using offset well data and expected reservoir properties. This pre-drilling model is used for ICD candidate screening and economic feasibility analysis. During the drilling operation, the model is then updated several times with the actual wellbore trajectory and real-time logging-while-drilling (LWD)-derived reservoir data in order to optimize the final completion design. Finally, a post-drilling evaluation can be conducted with the updated single-well dynamic model to evaluate the ICD performance.

This simplified workflow for single-well dynamic modeling has enabled quick turnaround time for practical ICD design optimization during real-time execution. An assessment of the well performances based on analytical analysis and numerical simulation has shown that ICD completions result in better sustained well productivity and incremental oil recovery compared to openhole and screen completion.

Deliquification of High-Temperature Gas Wells

Liquid-loading is a common problem in many gas wells in the Gulf of Thailand (Sompopsart et al. 2017). At the late stage of well life, reservoir pressure declines and gas and production rates and velocity in the tubing decrease. Liquids (condensate and connate fluids) begin to collect on the walls of the tubing and accumulate at the bottom of the well, eventually killing the well.

Successful deliquification of gas wells is a primary means of prolonging economic field life and increasing remaining reserves of depleted gas reservoirs. For offshore wells, well intervention methods to unload liquids include using coiled tubing and recompletion to install gas lift mandrels and chemical treatment.

A field trial was conducted by PTTEP and Halliburton to explore the use of high-temperature foam-assisted lift (FAL) as a method to unload liquid from wells. This technique provides an alternative to using coiled tubing for nitrogen lifting, or recompletion to install gas lift mandrels, which are expensive and involve operational risk. The challenge in using FAL in the Gulf of Thailand is the high downhole temperature. Special foaming agents that can operate at 450°F (232°C) were used for the trial.

The foaming agents work by forming a stable foam in the tubing, which lightens the fluid column and allows the produced gas to lift excess fluids that were previously preventing flow to surface. Computer simulations are used to calculate the efficiency of foaming agents and optimize the quantities to be used. Lab experiments are also conducted on fluid samples from each well, such that the foaming agent is designed for specific well conditions and fluid compositions.

The results of the field trial were promising. Three wells that were previously unable to flow to the production system we reactivated, and another well was capable of flowing at higher rates than it was achieving prior to the FAL job. Only one of the five wells involved in the field trial did not respond to the treatment. The low-cost FAL technique has demonstrated its ability to enhance mature gas field production, and is planned to be applied to other offshore fields in the Gulf of Thailand in the near future.

Predicting Pipeline Corrosion by Artificial Neural Networks

Artificial neural networks (ANN) are increasingly finding more applications across the oil industry. ANNs are composed of multiple nodes, which imitate the biological neurons of the human brain. The neurons in the human brain are linked and interact with each other.

In ANN, the nodes are connected by links. Each link is associated with a weight. ANNs are capable of learning, which takes place by altering weight values.

Top-of-line corrosion (TOLC) is the major corrosion problem in pipelines in the Gulf of Thailand (Silakorn 2016). Conventional corrosion models cannot predict TOLC accurately. PTTEP has applied ANN to model interfield pipelines in the Gulf of Thailand with the objective of getting more accurate metal loss prediction.

The basic steps in building the ANN pipelines model consist of data preparation, designing the network architecture, iteration of learning and validation, and modifying the weights accordingly. Parameters for corrosion rate calculation are used as the input of the architecture. The prediction model is finally verified by predicting other pipelines whose data have not been used in the process.

Results from the ANN model and those from the conventional model were compared with actual field measurements. The results show that the ANN model could predict TOLC more accurately than those from conventional simulation models by a factor of 2.8 to 6.5.

Environmental Impact of Decommissioning Options of Offshore Platforms

The Gulf of Thailand is rich with marine life, and the fishing industry is an important part of the Thai economy. Thailand is one of the top fish-producing nations in the world (FAO 2017). With its tropical warm water, the Gulf of Thailand is a popular tourist destination for scuba divers.

Because of the large number of offshore platforms already installed in the Gulf of Thailand, PTTEP is evaluating options for decommissioning platforms after abandonment. An important factor in selecting a decommissioning option is the impact on the marine environment of the Gulf.

The approach adopted uses the concept of net environmental benefit analysis (NEBA). Net environmental benefits are defined as the gains in environmental services or ecological properties achieved by an action minus the impact or injuries caused by that action.  

Ten ecological criteria were considered, which include factors that reflect the well-being of the marine life in the Gulf such as air, water, and sediment quality; fish habitat; fish production; marine mammals and reptiles; and coastal habitat, among others. 

A case study was done by ranking six different options for decommissioning offshore platforms (Kanmkamnerd et al. 2016). Some of the options considered were:

  • Removal of jacket, mudmat, and piles above mudline and onshore disposal
  • In-place reefing of the jacket
  • Tow and reef the jacket at an alternate location

The merits of each option are scored against the ecological criteria using a weighting system.
The result of the NEBA approach is a qualitative ranking of decommissioning options in the context of environmental benefits of each option. It provides decision makers with an efficient and cost-effective tool to examine alternatives for decommissioning.

There is awareness among operators in the Gulf of Thailand of the importance of sustainable development of the oil and gas industry by carefully balancing the economic, social, and environmental aspects to develop the resources with minimal cost and impact on the environment.


ANN               Artificial neural networks

EIA                  Energy Information Administration

FAL                 Foam-assisted lift

FAO                Food and Agriculture Organization of the United Nations

ESP                Electrical submersible pump

ICD                 Inflow control device

LWD               Logging while drilling

NEBA             Net environmental benefit analysis

PTTEP           PTT Exploration and Production (national oil company of Thailand)

TOLC              Top-of-line corrosion

For Further Reading

US Energy Information Administration website:

United Nations Food and Agriculture Organization (FAO) website:

IPTC 18786. 2016. Three Generations of Wellhead Platform by P. Phanichtraiphop et al.

SPE 185377. 2017. The First Application of Specialty Sand Screens in Combination with Fully Integrated ESP Technology in the Nong Yao Field, Gulf of Thailand by I.T. Chigbo et al.

IPTC 18848. 2016. ICD Design Optimization with Single-Well Dynamic 3D Modelling and Real-Time Operation Support by L. Van der Bol et al.

IPTC 18772. 2016. A Success Story for a High-Temperature Foam-Assisted Lift Application in a Mature Field, Gulf of Thailand by S. Sompopsart et al.

IPTC 18658. 2016. The Application of ANN Artificial Neural Network to Pipeline TOLC Metal Loss Database by P. Silakorn et al.

IPTC 18982. 2016. NEBA Application for Jacket Decommissioning Techniques by J. Kanmkamnerd et al.