The fluid rates and bottom-hole flowing pressure of the wells are essential parameters in the petroleum industry. The need of accurate readings of these measurements are necessary for many calculations such as gas-lift optimization, field monitoring and depletion plans. Predicting these parameters without running in hole has a good impact on reducing the intervention risk and on organization financials by saving time and money. Huge number of correlations are used to estimate these parameters. These correlations need the values of different parameters that are not accurately found. Therefore, an artificial neural network (ANN) model was built from exported data set of PROSPER
Abu Bakar, Azfar Israa (Petronas Carigali Sdn Bhd) | Ali Jabris, M Zul Afiq (Petronas Carigali Sdn Bhd) | Abd Rahman, Hazrina (Petronas Carigali Sdn Bhd) | Abdullaev, Bakhtiyor (Petronas Carigali Sdn Bhd) | Idris, Khairul Nizam (Petronas Carigali Sdn Bhd) | Kamis, Azman Ahmat (Petronas Carigali Sdn Bhd) | Yusop, Zainuddin (Petronas Carigali Sdn Bhd) | Kok, Jason Chin Hwa (AppSmiths® Technology) | Kamaludin, Muhammad Faris (AppSmiths® Technology) | Zakaria, Mohd Zulfadly (AppSmiths® Technology) | Saiful Mulok, Nurul Nadia (AppSmiths® Technology)
Field B, located offshore Malaysia is heavily reliant on gas lift due to the high water cut behavior of the reservoir coupled with low-medium reservoir pressure. The field faces a challenge to efficiently execute production enhancement activities due to its low effective man-hour, a drawback of unmanned operation philosophy. The recent oil price downturn further exacerbates the limitation and calls for an innovative approach to continue the effort for maximizing oil recovery.
As majority of the producing wells are gas-lifted, Gas Lift Optimization (GLOP) is an integral part of Field B's routine production enhancement job. The previous practice of GLOP involves data acquisition process of surface parameters and wireline intervention to collect Bottomhole Pressure (BHP), mainly Flowing Gradient Survey (FGS). Relying on wireline intervention limits the number of gas lift troubleshooting activities due to the low man-hour availability. To address this constraint, CO2 Tracer application was implemented in a campaign to supplement Field B GLOP effort. CO2 Tracer is a technology whereby concentrated CO2 is injected into the gas lift stream via the casing. CO2 returns are collected at the tubing end and utilized to diagnose the gas lift performance.
The CO2 Tracer campaign was successfully executed in Platform A, B and C, covering 58 strings within an effective period of 3 months. This achievement is a milestone for the field as it opens a new approach in GLOP data acquisition process. Several advantages observed by executing this campaign is as follows: Multiplication of opportunities generation due to quick and simple operations of CO2 Tracer survey compared to wireline intervention for FGS. Reduction in HSE risks and intervention-related well downtime due to minimal intrusive requirement for well hook-up. Better understanding of complex dual gas lift completion due to simultaneous survey execution. Supplement CO2 baseline measurement for flow assurance monitoring. Quick quality check on gas lift measurement device.
Multiplication of opportunities generation due to quick and simple operations of CO2 Tracer survey compared to wireline intervention for FGS.
Reduction in HSE risks and intervention-related well downtime due to minimal intrusive requirement for well hook-up.
Better understanding of complex dual gas lift completion due to simultaneous survey execution.
Supplement CO2 baseline measurement for flow assurance monitoring.
Quick quality check on gas lift measurement device.
This paper will discuss on the challenges at Field B to implement GLOP, technology overview of CO2 tracer, the full cycle process of the CO2 tracer campaign and results of the campaign. Several examples of the findings will also be shared.
Ekkawong, Peerapong (PTT Exploration and Production Plc.) | Kritsadativud, Pannayod (PTT Exploration and Production Plc.) | Lerlertpakdee, Pongsathorn (PTT Exploration and Production Plc.) | Amornprabharwat, Anan (PTT Exploration and Production Plc.)
Gas fields in the Gulf of Thailand (GOT) share some similar operational complexities and experience many common challenges. Such challenges include the huge number of wells and platforms, and the large, complex, interconnected pipeline network. Additionally, each well, of course, exhibits different performance, different enhanced recovery as well as different and diverse flow assurance methods. Fluid streams also vary significantly from well to well; for instance, the differences in condensate to gas ratios (CGR), water to gas ratios (WGR), and the CO2, and H2S levels. Moreover, production performance in the GOT remains very dynamic. The decline in production could be seen early, even though proper reservoir management was achieved because most of the reservoirs were small and compartmentalized. Optimizations aiming to maximize revenue from these fields are very challenging.
State-of-the-art industry solutions to these problems are provided by integrated production modeling, and reservoir simulation. At first consideration, they appear to be reasonable tools that can physically describe the flow of fluid, whether in a reservoir, well or surface facility; however, these tools may not serve well for the complicated compartmentalized characteristics of the gas fields in the Gulf of Thailand. Currently, determining optimum natural gas production rates in the GOT is performed by manually fine-tune the production rate using information from the latest well testing data. This method may simple and convenient but requires large effort and does not guarantee the optimal solution.
This study presents a more efficient production optimization scheme integrating constrained optimization with decline curve analysis to predict future well production performance. The project net present value is translated into the objective function, comprising maximizing condensate production and minimizing waste water production while also honoring daily gas production nomination. Well performance, export specification, and the capacity of pipeline networks are formulated as system constraints. A linear programing optimization algorithm is then used to solve the resulting optimization problem for a single time step. Next, the optimization is integrated with the production decline trend from the decline curve analysis to obtain the forecast of future production performance.
Tested against the production data of a large gas field in the Gulf of Thailand, this method showed a significant increase in the condensate production and a decrease in the water production. This solution not only enhanced production, but also reduced tedious time required for modeling, history matching, or manually configuring well production. Main assumptions, limitations and the conclusion of the proposed method are also included in this study.
Al-Ruheili, Sharifa Moh'd (Petroleum Development Oman) | Angelatos, Matthew (Petroleum Development Oman) | Ramakrishnan Nair, Sujith Kumar (Petroleum Development Oman) | Peringod, Chandran (Petroleum Development Oman) | Sonti, Kartik (Shell India Markets Private Ltd) | Karacali, Ozgur (Schlumberger Oman & Co LLC)
In Auto gas-lifted wells, gas from either a gas zone or gas cap is used to lift oil in a commingled way. Balancing gas zone energy depletion to the benefit of oil production requires an accurate understanding of zonal contribution and an ability to adjust and control inflow from both zones.
A project was undertaken for accurate zonal allocation and optimization of lift gas rates by designing an appropriate zonal contribution measurement procedure for five Auto gas-lifted wells on two fields. This approach utilized both production logging and production testing to obtain both zonal down hole and commingled surface flow measurements. As a result of the exercise, lift gas flow into the tubing was optimized, thus maximizing the oil production rate while conserving reservoir energy through efficient gas usage.
Measurement of zonal contributions in multi-zone intelligent well completions are often challenging due to the complex nature of fluid flow under varying dynamic conditions of multiple reservoirs. This paper will explain the zonal contribution measurement program design, implementation, interpretation of data and lessons learnt from the project.
The B Field is a part of the North Asset of Petroleum Development Oman. This field was discovered in 1973 and put on-stream in 1986. The three reservoirs, Upper G1, Middle G2 (both low net to gross fluvial sandstones) and Lower G3 (thin, high net to gross shore face sheet sands) have very light oil (42 API, 0.32-38 cP) contained in 13-57 m thick oil rims at depths of about 2500-2700m ss.
A Field Development Planning (FDP) study to investigate further development strategy in the B field was completed in 2004. The plan included drilling 32 production wells and 4 dedicated gas injection wells, a water injection pilot and contingent follow-up water injection phases. Ultimately, gas-cap blow-down was proposed for the 2015 timeframe.