Petrovietnam Journal

Hai Thach - Moc Tinh, high pressure/high temperature fields in the first ten years of production Hai Thach - Moc Tinh: Ten years of production, and solutions to maintain and increase its output in the coming period

2024-04-27T08:10:48+00:00

Based on the relevant technical and engineering data, the article presents the main geological and geophysical characteristics of the field cluster of Hai Thach - Moc Tinh, and summarizes its development and production management to date. After 10 years putting the Hai Thach - Moc Tinh cluster into operation under particularly complex conditions, Bien Dong Petroleum Operating Company (Bien Dong POC) has achieved important results, including: (i) Building and developing a simulation model to optimize production operations; (ii) Applying advanced well completion techniques suitable for high pressure and high temperature conditions; regularly conducting technical improvements throughout the operation and maintenance process; (iii) Minimizing the impact of liquid banking near the well bottom, limiting sand production and water encroachment in the wells and exploitation system, increasing reservoir water treatment capacity; (iv) Studying to propose potential locations for infill, sidetrack and multi-lateral wells for improving recovery rate, extending the field lifetime while also serving as a basis to expand the exploration in the area. Despite these achievements, Bien Dong POC is facing the decline of the field's output and recovery rate due to fluid banking near the well bottom, water encroachment and sand production. Therefore, adjusting the field development plan and accelerating expanded exploration are the solutions to maintain and increase the field's output in the coming time.

Analyzing the probability of success, ranking the potential of the remaining exploration prospects in study area, Nam Con Son basin, and proposing further plans

2024-04-27T08:10:48+00:00

petroleum prospect, especially for those in areas with unique characteristics, complex geology, and high drilling cost. In the article, the probabilities of geological success of the structures are analyzed based on the results of seismic interpretation, well logs, important geological parameters, etc., then combined with the hydrocarbon initial in place (HIIP) to rank, from high to low potential, the remaining prospects of the study area in the Nam Con Son basin.

The economic assessment shows that there are 3 most potential prospects with positive EMV, which are being considered targets for the next expanded exploration campaign in the study area. Given that they are located in the area with complex geological characteristics, high drilling costs, and unforeseen risks since the existing 3D seismic data were acquired and processed many years ago, etc., the authors have proposed some further exploration steps for the selected prospects.

Application of process increasing the reliability, accuracy in 3D seismic interpretation to make structural maps and predict the sand distribution for the Upper Miocene turbidite in Moc Tinh field, Block 05-3, Nam Con Son basin

2024-04-27T08:10:48+00:00

The upper Miocene turbidite sand UMB15-20 is the main gas - condensate reservoir having very good porosity and permeability in the Moc Tinh field. Reflection seismic data and the physical model calculations showed that the top and bottom of this sand reservoir reflected seismic waves with AVO class III or class IIp attributes inconsistently in all 8 drilled wells, which caused some risks in determining the corresponding seismic reflections to map the top/bottom geological structure of the reservoir and predict the sand distribution by conventional seismic interpretation methods. Bien Dong POC has applied the interpretation - analysis process by integrating rock physics, AVO model, pre-stacking seismic inversion, and verified with results of petrophysics - seismic justification to assess the accuracy and reliability of seismic attribute cubes, and cut-off values corresponding to reservoir/non-reservoir type; thereby, the mapping and predicting of the reservoir sand distribution of the field were conducted. The results of the above application have established a product set including the top/bottom maps of the UMB15-20 reservoir and the boundary of the sand distribution with the highest reliability. This map set was used for HIIP calculation in the 2021 Resource Assessment Report and static/dynamic modeling to efficiently support the field operation and management. In addition, these data are also used to optimize the location of Bien Dong POC's upcoming infill drilling wells.

Effects of high pressure and high temperature on thickening time of cement slurry in the cementing process at Nam Con Son basin

2024-04-27T08:10:48+00:00

Thickening time is an important parameter that affects the well cementing process - a decisive step in the quality and efficiency of well operation. During the pumping process, the thickening time of the slurry needs to be greater than the pumping time. Otherwise, problems will occur due to cement curing prematurely before technological processes are implemented, easily leading to complications and incidents, causing waste of materials and costs, and prolonging construction time. Currently, to construct the oil and gas wells, G-grade Portland cement - a popular type of cement for deep well constructions - is normally used. However, under high pressure and high temperature conditions in the Nam Con Son basin, it is necessary to add silica heat-stable additives to ensure the thickening time of the cement slurry is consistent with the conditions of the well.

Real-time production management system: A case study in Hai Thach - Moc Tinh fields

2024-04-27T08:10:47+00:00

Bien Dong Petroleum Operating Company utilizes a production data management system (PDMS) leveraged by digital technologies such as machine learning, artificial intelligence, and big data to collect, transmit, and process technical data for the cluster of Hai Thach - Moc Tinh fields. The PDMS combined with a dashboard tool supporting real-time intelligent production management has streamlined the data collection, monitoring and evaluation of the production process, field behavior control and exploitation optimization. By this tool, exploitation plans suitable to the buyer's gas mobilization needs as well as optimal solutions for production engineers have been proposed. This approach helps to minimize unplanned downtime, enhance production and processing efficiency, ensure safe and continuous operation, and yield economic benefits for the natural gas processing systems at the Hai Thach field.

Application of machine learning to decline curve analysis (DCA) for gas-condensate production wells with complex production history due to add-on perforation of new reservoirs

2024-04-27T08:10:47+00:00

For every oil and gas operator, DCA plays an essential role since it provides crucial information for production planning and reserves estimation. DCA is the analysis of the decline in production rate or pressure over time, which can be done by fitting a curve through production or pressure historical data points and making a forecast for the well based on the assumption that the same declining trend will continue in the future. However, the conventional DCA method has been shown to have some limitations. On the other hand, machine learning has been vigorously and extensively researched in the last decade; its applications can be found in every aspect of life as well as in the oil and gas industry. Therefore, it is the ideal time to study the application of machine learning to DCA, to complement this important analysis. In this case study, machine learning was used to predict the decline of wellhead pressure, thereby determining well life as well as estimating reserves. The method was applied to 2 wells with very complex production histories due to add-on perforation of new reservoirs. The prediction was verified to have high reliability by comparison with dynamic modeling results.

Application of machine learning to predict the time evolution of condensate to gas ratio for planning and management of gas - condensate fields

2024-04-27T08:10:47+00:00

One of the most important parameters for evaluating, forecasting, and managing gas - condensate fields is the evolution of the condensate to gas ratio (CGR) over time. This parameter tends to decrease as reservoir pressure declines. Conventionally, gas and condensate samples are collected initially at the time starting production and periodically later to conduct laboratory analyses of fluid composition, properties and CGR. However, sampling, transporting and analysing samples take time and effort and, therefore, could be very expensive. To predict CGR over time, likewise, dynamic models are also frequently used. However, these models could include many uncertainties due to the assumption of input data, including reservoir structures, fluid phase interaction, and reservoir property distribution. Therefore, application of machine learning to predict the time evolution of CGR in this research is a new and effective approach to supplement conventional methods.

Developing a machine learning tool to predict discharge temperatures of gas compressor

2024-04-27T08:10:47+00:00

Gas compressors are important equipment on the central processing platform PQP-HT. After dehydration and ensuring the dew point temperature in accordance with inlet conditions and specifications of the Nam Con Son gas pipeline (NCSP), natural gas is transferred to a gas compression system consisting of 2 compressor lines. Optimizing operating conditions by reducing the inlet pressure of the natural gas processing system is normally used to extend production time of a gas well. However, alterations in inlet operating conditions will directly affect the gas compressor system, potentially causing the discharge temperature to exceed safe operating thresholds.

Commercial thermodynamic simulation software (such as Hysys, ProII) is typically employed to assess the effect of changing gas compressor operating conditions on the outlet temperature of each stage. This allows simulation and selection of optimal working conditions to ensure safety within the natural gas processing system. Nevertheless, the cost of licensing and maintaining commercial software is substantial. Nowadays, machine learning algorithms are proven to be able to predict operating parameters based on historical data. Many studies have been devoted to accurately predicting compressor performance to improve operational efficiency. Machine learning algorithms have the advantage of highly precise prediction results and the ability to operate continuously and re-train automatically upon any operational condition change. Therefore, they can be used as a viable alternative to commercial thermodynamic simulation software.

Prediction of the remaining useful life for plate heat exchanger at Hai Thach - Moc Tinh fields

2024-04-27T08:10:47+00:00

Predictive maintenance is an advanced and widely adopted approach in the industry that helps maximize the equipment uptime by estimating its remaining useful life (RUL) and predicting any potential failure point. The authors have made a short-term prediction of the seawater flow pressure difference at a plate heat exchanger using a long short-term memory (LSTM) network, and thereby predicted the RUL using a nonlinear regression model. The proposed model achieved high accuracy by continuously detecting checkpoints and predicting RUL values every 24 hours. Checkpoints are identified through detecting differential pressure anomalies at the plate heat exchanger during operation. Thereby, it helps update the RUL value promptly upon any unforeseen deviation during equipment operation.

Developing a time scheduling algorithm for maintenance tasks: a case study at Hai Thach - Moc Tinh fields

2024-04-27T08:10:47+00:00

Using dataset sourced from the maintenance management application of Bien Dong Petroleum Operating Company (Bien Dong POC), the authors studied to employ genetic algorithms to schedule maintenance tasks, thereby shortening planning time, optimizing the involved resources, ensuring the consistency of the maintenance planning and implementation. Findings indicate that genetic algorithms effectively generate schedules with a high degree of accuracy, addressing multiple constraints related to time, priority, and resources for each task code, while significantly reducing computation time compared to manual scheduling methods. The study manifests the potential for implementing tools that automatically deploy such algorithms to support engineers, enhancing the efficiency and precision of maintenance planning and management process, especially at large-scale production facilities.