Gas condensate reservoirs exhibit complex flow behavior below the dewpoint pressure, caused by compositional changes and the creation and growth of a condensate bank around the wellbore, which effectively reduces the relative permeability to gas flow. As a result, gas production decreases and liquid condensate, a valuable resource, is left behind in the reservoir.Well deliverability impairment resulting from liquid dropout has been a main focus of gas condensate studies for over 60 years. We have used compositional reservoir simulation, with representative lean-and rich-condensate fluids and velocity-dependent relative permeability models, to predict condensate dropout under typical operating conditions. The effectiveness of different production methods and remediation solutions in minimizing condensate buildup below the dewpoint pressure was quantified, using first a single-well model and then full-field models of tight and low-permeability gas condensate reservoirs in which six vertical wells were compared with two horizontal wells.We found that, although both horizontal wells and vertical well stimulation do improve well productivity, productivity enhancement depends greatly on well and reservoir parameters such as horizontal well lengths, well placement, reservoir permeabilities, and gas condensate compositions. For gas condensate production below the dewpoint pressure, it is possible to achieve an optimum balance between gas production rate and pressure drawdown, thus minimizing condensate dropout effect while producing at a reasonable rate. In a low-permeability gas condensate reservoir, the six vertical wells perform slightly better than the two horizontal wells for the same amount of gas production. In a tight gas condensate reservoir, on the contrary, gas recovery with two horizontal wells is significantly greater than with six vertical wells. Although, in this model, well stimulation can increase the productivity of the vertical wells in tight gas condensate reservoir, it is not as effective as using horizontal wells.

BD Cluster green fields development located offshore Sabah, Malaysia, consists of three multi-stacked turbidite fields, namely A, B and C, encompassing thick and thin bed sands. Due to the lack of existing infrastructure in close proximity, a wellhead platform (WHP) will be installed on top of Field A. Fields B and C will be developed with a respective 8 and 7km subsea tie back to this WHP. Gas will be exported from the WHP to Facility-1 situated 5km away, whereas oil from a single thin oil rim reservoir in Field A will be exported to Facility-2 50km away. The challenges faced by the Reservoir Engineering (RE) Team was delivering an extensive number of dynamic simulations while adhering to the Field Development Planning (FDP) submission deadline: 1) uncertainty analysis and probabilistic modelling for 9 models, 2) construction of coupled reservoir models 3) screening alternative oil and gas export routes, and 4) optimizing capex phasing by determining the optimum startup sequence of the fields. Delivering the FDP work on time with the limited software licenses and computing infrastructure available on-premise appeared to be a "bridge too far". The limitations were addressed by PETRONAS LiveFDP digital transformation initiative commenced in 2019, through deployment of digital cloud technologies and solutions with scalable High-Performance Computing (HPC) environment. A total of 9 geological models were delivered to REs for dynamic simulation studies. Probabilistic modelling was then employed to obtain the dynamic P10, P50 and P90 models for each field. The Reservoir Coupling facility and Extended Network option were used in the numerical simulator to couple the standalone models in order to honor the overall facility constraints and incorporate the pipeline effects. Utilizing the coupled network model, multiple studies including condensate banking, determining optimum field sequencing and export route scenario were performed. The FDP subsurface development simulation runs were completed within 1 month using HPC cloud solutions and workflows compared to 9 months if using on-premise infrastructure. It provided the necessary tools to allow the team: 1) accurately assess the impact of condensate banking on well productivity, 2) executed over 1200 cases for probabilistic modelling for the 9 models in 24 hours of simulation time, 3) reduced the number of wells derived from a previous study from 14 to 9 yielding a saving of ~US$115 million, 4) ~US$50 million savings as a result of capex phasing by optimizing the field start up sequence, and 5) US$130 million savings by establishing the lowest cost oil and gas export route scenario.

PETRONAS Baronia field is a mature oil field with over 45 years of production history, located offshore Sarawak, Malaysia. It consists of several vertically stacked clastic sandstone reservoirs, namely two major reservoirs: S and V2 reservoirs. Both reservoirs have been on production since 1970's with the production strategy evolving over the years to maximize recovery. Natural depletion, infill drilling, water and gas injection, and recently Immiscible Water-Alternating-Gas (IWAG) IOR/EOR strategies have been implemented. All these elements combined with the subsurface uncertainties pose challenges to history match and to conduct probabilistic forecast studies on the dynamic models. Conventionally, the development scenarios for subsurface investigation are limited due to finite computing resources. As PETRONAS is shifting its portfolios to develop more complex and challenging fields, the need for transformation in development concept evaluation is evident. This is key for proper risk and uncertainties quantification. The notable challenges are a) limited number of development scenarios being investigated, evaluated, and compared; b) limited software licenses and infrastructure availability; c) lack of data and decisions traceability. These limitations are addressed by the PETRONAS LiveFDP digital transformation initiative commenced in 2019, through deployment of digital cloud technologies and solutions with scalable High- Performance Computing (HPC) environment. The cloud-based native and Petrotechnical applications enable remote work, ensure full data traceability and auditability, enable multi-realization ensemble analysis, and streamline the automated integration from the reservoir engineering ensemble workflow to economic analysis. Unlimited cloud computing power and licenses facilitate a broader spectrum of reservoir simulation cases to be investigated in a fast-tracked manner. The cloud HPC infrastructure has shortened the history matching cycle from 3 months to 1.5 months. The team has also observed over 5 times speed enhancement on simulation run performance using cloud computing compared to virtual machine and on-premise infrastructure. Utilizing the cloud solutions and ensemble probabilistic approach, the team has achieved over 90% of history match quality through 300 realizations per ensemble running concurrently and completed within 2 hours. The optimized IWAG injection resulted in 2% (~1MMStb) higher oil reserves with 37% less gas injection and 40% shorter injection cycles. This has improved gas sales and prioritization in the field while also monetizing the oil reserves. The ensemble analyses are then visualized using cloud-based data analytics system whereby key realizations and uncertainty parameters are further reviewed and highlighted across various disciplines collaboratively at real time.