Mazzlan, Khairul Akmal (PETRONAS) | Chia, Mabel Pei Chuen (PETRONAS) | Tamin, Muhammad (PETRONAS) | Tugimin, M Azri B A (PETRONAS) | Azlan, Ali Al-Amani (PETRONAS) | Michael, Lester Tugung (Schlumberger) | Sepulveda, Willem (Schlumberger) | Cortez V., Juan L. (Schlumberger) | Muhamed Salim, Muzahidin (Schlumberger) | Kalidas, Sanggeetha (Schlumberger) | Chan, Nathanael Vui Kit (Schlumberger) | Biniwale, Shripad (Schlumberger) | Serbini, Feroney (Schlumberger) | Mohd Arifin, Azahari (Schlumberger) | Tan, Tina Lee Ting (Schlumberger) | Tee, Karen Ying Chiao (Schlumberger)
‘S’ field is a mature oilfield located offshore Sabah, Malaysia. As part of the redevelopment plan, ‘S’ field was the first field selected for an end-to-end asset management Integrated Operations (IO project) where multiple workflows have been implemented for the asset operation optimization through monitoring and surveillance. One of the exclusive workflow that will be further elaborated in this paper is on Candidate Selection and Reservoir Optimization.
Although field optimization mission was ongoing, proper knowledge capture and standardization of such techniques were not adequate due to the limited data management. Lack of decision-support mechanism and most importantly the challenge was of understanding and analysing the asset performance. A key to the success of field and reservoir optimization is defining a tailored approach, for selection of right candidate and collaborative decision for well/field intervention.
With an objective of full field revitalization, the project was focused on integrated, collaborative 3R approach – Reliability, Reusability and Repeatability. Reliability component was based on capturing knowledge from experienced professionals from various domains and blending that with traditionally proven analytical techniques. Reusability was emphasized by the development of consistent and robust analysis workflows ready to use. Repeatability was aiming at standardizing the process of candidate selection and decision making to assist junior engineers.
Chia, Mabel Pei Chuen (PETRONAS) | Yakup, M Hamzi B (PETRONAS) | Tamin, Muhammad (PETRONAS) | Surin, Nicholas Aloysius (PETRONAS) | Mazzlan, Khairul Akmal B (PETRONAS) | Rinadi, M (PETRONAS) | Hassan, A Azim B (PETRONAS)
This paper details out the application of a predictive analysis tool to'S' Field's commingled production, aiming to enhance production allocation and reservoir understanding without the need of well intervention and a reduced frequency of zonal rate tests and data acquisition. Allocation of the production data to its respective reservoirs is performed via a novel Multi-Phase Allocation method (MPA), taking into account the water production trending evolution derived from relative permeability behavior of oil-water in each reservoir to compute flow rates for liquid phases over time. The precision of the derived rates is constrained by actual zonal rates tests through Inflow Control Valves (ICVs). This method will be cross referenced against'S' Field's existing zonal rate calculation algorithm, utilizing input data from well tests results and real time pressure and temperature data. The MPA method demonstrates improvement in the allocation of production data as compared to the conventional KH-methodology as MPA takes into account the water cut trending between reservoirs. Leveraging on ICVs to obtain actual zonal rate measurements, this greatly reduces the range of uncertainty in the allocation process. MPA derived production split ratios closely match the split ratios derived from the'S' Field's existing zonal rate calculation algorithm, which utilizes input data from well tests results and real time pressure and temperature data from down hole gauges. It is observed that the usage of actual measured zonal rate tests reduces the range of uncertainty of the MPA data. A combination of novel multiphase deliverability models coupled with smart field technologies such as intelligent completions and real-time surveillance and analysis tools will increase the accuracy of the back allocation of multiphase production data in commingled reservoirs.
Das, Gunajit (Halliburton) | Khan, Hasnain (Halliburton) | Singh, Chander Shekhar (PETRONAS Carigali Sdn. Bhd.) | Mandal, Dipak (PETRONAS Carigali Sdn. Bhd.) | Kumar, Sanjiv (PETRONAS Carigali Sdn. Bhd.) | Tamin, Muhammad (PETRONAS Carigali Sdn. Bhd.) | Yunos, Khairil Anuar B Md (PETRONAS Carigali Sdn. Bhd.)
The field in study has multiple stacked reservoirs with 10-20m oil column overlain by medium to large gas caps. PVT analysis from DST in the gas zones were available, which showed gas and condensate production at surface. Composition, PSAT and CCE data were available from surface samples. DST tests in oil zone provided only GORs, surface oil and gas gravities. Reservoir oil and gas gradients were also available from RFT/MDT.
The challenge was to generate representative PVT properties for the oil zone for reservoir simulation by integrating these partial datasets. Due to condensate production, it was necessary also to incorporate and characterize the vaporized oil fraction in the gas, which is not possible by using black oil correlations.
This paper describes the workflow used to integrate these partial datasets through an EOS model to generate representative PVT properties for the reservoir oil and gas. The composition from the gas cap sample was used to build an EOS Model which was tuned using CCE data and gas cap DST results. This partially tuned EOS was used to simulate a compositional gradient experiment which was tuned to match the PSAT at GOC obtained from RFT/MDT and equilibrium oil composition at reservoir temperature and pressure at GOC was calculated. This oil composition was then plugged back into the EOS Model and model was further tuned with reservoir oil density from RFT/MDT, oil API and production GOR from the oil zone DST. This tuned EOS was then used to export Live Oil and Live Gas PVT tables for simulation.
This approach helped to generate representative PVT tables which produced a good history match in the dynamic model within the range of uncertainty. This approach is now being tried with other reservoirs where PVT data is inadequate and is becoming helpful to tune PVT models.