Field development for brownfields nearing their economic thresholds is always challenging, especially in offshore environments. As an operator, innovative approaches are necessary to reduce capital expenditures (CAPEX) and create attractive projects. A marginal cluster consisting of three fields, namely PN, NL, and PR, is expected to reach its economic limit in the next 2 years. This paper elaborates on single-trip completion technology as a catalyst for drilling one infill well in the PR field development project. In 2017, one appraisal well was drilled in a western area of PR field to validate the presence of oil. The scope of work included evaluating reservoir productivity and acquiring bottomhole fluid samples. A drillstem test with four multirate tests was executed for this reservoir. A horizontal development well named PA-02 was proposed and categorized as an extended-reach drilling well because of the drilling complexity. Most offshore wells in shallow-water environments are completed with a conventional well completion run that takes two or more trips, which normally takes more than between 5 and 8 days. Given expensive daily rig rates, the ability to reduce completion installation time was deemed vital to the economics of the project. If the installation incurs additional unnecessary project costs, it can cause the project to be economically unattractive. Using a collaborative approach, an interventionless, single-trip sand control system was designed and selected as the optimal completion solution to meet project demands. Radio-frequency identification (RFID) technology is one of the key enablers for the single-trip completion as it offers the utmost flexibility in both activation and contingency methods to deliver the necessary project cost reduction. At a time of uncertain global crude oil prices, the RFID-enabled single-trip completion concept discussed in this paper has become a beacon of light for operators in an otherwise dark period by allowing previously marginal or sub-economic projects to become viable. This technology has resulted in operational time savings of at least 27% compared to typical conventional two-trip completions in Malaysia offshore environments. Minimizing operational risk is also foreseen by reducing installation to a single integrated upper and lower completion trip. Selecting this RFID-enabled completion facilitated full deployment in one trip in the high-angle well, which eliminated the deployment of a tractor service for a 67% cost savings in this aspect alone. This method represented a paradigm shift in operational efficiency and will now be the operator’s strategic completion methodology when developing marginal fields. The deployment represents the first application of a single-trip completion in an economically challenging brownfield in the Malaysian offshore environment. The reduction in operational time and resultant savings in CAPEX proves that a single-trip completion offers an exceptional alternative to conventional methods in the shallow-water offshore environment.

This paper discusses the workflow and method of selecting optimum number of infill and injection wells based on incremental recovery. Normally, for infill wells study, a ‘creaming curve’ method is used to evaluate the optimum number of wells against incremental recovery from the field. However, in the case of determining number of infill wells together with water injection wells, a more comprehensive approach is needed. One needs to evaluate the pressure depletion rate from existing and infill wells together with the dynamic of the producer-injector pairings as well as the sweeping factor. The paper is based on infill and water injection development plan for a brown field in Sabah basin which located in Malaysia. To maintain operatability of the field in the future, several new infill and water injection wells options are evaluated for optimum field life oil production. Unlike infill or producer-only assessment, the same ‘creaming curve’ approach for combination of infill and water injection wells is less effective as large number of simulation runs are needed to sample the combination of these wells that generate optimum oil recovery. This has proven to be challenging especially when the models are large which is normally the case for brown fields and it requires extensive computational hours. In the first part, a modified approach bringing some pre-analytic assessment of producer-injector pairing is being used. The pairings are first ranked based on streamlines visualization, drainage tables and their respective contributions towards oil recovery. The ‘creaming curve’ is then built based on the highest contribution as well as the sequencing of the pairings. The second method mentioned in this paper is the numerical approach through multi-objective optimization using assisted history matching and uncertainty tool. With the help of optimizer, the number of simulation runs can be drastically reduced when only best combination of infills and injectors for each total number of wells are considered. Both alternative methods will be compared with the full computational runs, sampling every single combination of wells. Finally, the optimum number of wells with the combination of infill and water injection wells are analysed based on cumulative oil recovery against the Net Present Value (NPV). This case study therefore demonstrates how alternative methods can be used to resolve the optimum number of infill and water injection wells to avoid lengthy and very large numbers of simulation runs.

PETRONAS Upstream is cognizant of the need to provide a unified digital production platform to the entire upstream community. The digital platform enables the community to perform analytical, collaborative monitoring & surveillance, and optimization to sustain production and improve our operations. Digital Fields (DF) is a modern digital platform, integrating data and analytical services, that provides smarter insights and shed light on potential insights that contribute to better-informed decision-making and improves the way of working. A big data ecosystem and strong infrastructure is the foundation of this digital platform, allied to existing business processes, it allows frictionless secure flow of data, high performance, scalability to support business operations. Digital Fields platform provides the following features: Scalable to all PETRONAS fields operated in Malaysia as well as International assets Solution architecture that allows fast implementation of new solutions and insights A common company-wide platform with the same familiar user interface and user experience The critical aspect to liven up a digital production platform is to identify first the available data sources. Some fields have the luxury of transmitters and sensors installed at the well head, topside facilities and export pipelines where the data can be easily retrieved from the SCADA system for data acquisition. Some other fields with less real-time data luxury would keep their operational data inside some operational reporting documents. A smart report ingestion solution was developed with the means to transform unstructured data from the operational reports, which empowers the users with multiple options for data upload and consumption while ensuring a single, traceable and auditable source of truth. Digital Fields leverages the combination of Daily Operation Reports (DOR), data with different frequency, such as real-time data, with engineering models changing the way the users consume data and provides proactive actionable insights to accelerate tangible values for the business organization. The solution establishes the foundational digital capabilities for field operations and speeds up data-driven value opportunities from operational data and analytics at scale.

Objectives/Scope Field A is a brownfield that has been produced for 52 years under natural depletion via a total of 207 strings. The field comprises 7000 ft of reservoir section, multiple fault blocks and over 200 separate reservoir units has been produced to date. Despite the long period of production, the field recovery factor (RF) to date is only 29%. To improve the RF, a strategic assest value framing exercise was carried out to identify the additional subsurface opportunities i.e. infill well drilling, secondary recovery, late field life appraisal in underdeveloped fault blocks as well as adopting standardized low well concepts and the design one build many facility design concept to reduce cost and accelerate development. The main purpose of the exercise was to capture the overall opportunities for the field, outline the roadmap and phase out the project with suitable wells and facilities design to bring down the cost for project commerciality. Methods, Procedures, Process The integrated workflow of the exercise involved subsurface, drilling, facilities, operations and economist and took a total of 3 months to complete. The process started off with a RF benchmarking exercise utilizing a newly developed inhouse RF benchmarking tool to compare the current attained vs the attainable RF(EUR) and identify incremental reserves. The number of new wells required to develop the incremental reserves was estimated by analyzing EUR per well trends over time. This analysis indicated that on a campaign basis the current realistic average EUR per well is in the order of 0.7-1.0 MMstb per well. The preliminary well placements are guided by bubble maps of all reservoirs, in a top to bottom & block by block approach, to identify underdeveloped areas and combining these areas into stacks of reservoirs that can be combined and developed with simpler wells from existing or future facilities. The drilling team has designed a few simple trajectories to penetrate shallower to deeper reservoirs and proposed the drilling center within a radius of 2 km from the targets. This approach differs from the object-based approach where individual high EUR targets are chased with more complex wells drilled from a specific location during a platform campaign. Result, Observations, Conclusions Potential additional reserves and 6 new projects have been identified which would result in a field recovery factor increase of 11%, of which 2 projects are being accelerated to realize early first oil. Timelines for all the projects have been mapped out with the aim of completing all within the next 10 years. A dedicated project management team has been formed to support the project from the initial stage. Detail Full Field Review study will be conducted to mature all the opportunities up to development stage. The listed projects will follow the low cost well guideline established in the framing as well as fast track facility design concept. Novel/added value The strategic value framing exercise is a systematic approach that provide a total picture of the future opportunities to optimize field production/EUR and maximize commercial value of brownfield redevelopment.

This paper showcases the tailored initiatives undertaken in a different economic climate in view of low oil price in maturing and capturing potential development opportunities for a large-sized, brownfield asset. The subject field is mature, having over 250 wells with over 20 years of production history and approaching its end of economic life. The study emphasized pursuit of a fast and cost-effective approach in identification of several potential behind-casing recompletion opportunities in addition to multiple infill drilling opportunities. From well log observation to seismic interpretation, and from material balance to simulation modeling, the approaches utilized were tailored based on the level of reservoir complexities. Lower cost, lower risk, behind pipe opportunities had previously not been a focus or thoroughly assessed since the company had focused on infill drilling opportunities. A robust execution plan, minimizing rig moves, was also prepared to synergize the ongoing and planned infill drilling campaigns in the field. An infill drilling well proposal was approved as part of the 2014 Field Development Plan (FDP) update with regulators. This plan was subsequently deferred due to drop in oil price resulting lower economics. However, with the field approaching the end of its economic producing life, a revisit of potential opportunities was conducted by a multi-disciplinary team. Results of the more recent opportunities identification and successful drilling campaigns have revived the proposal for the infill well given the synergies with rig utilization. The team went through a simplified governance process on the FDP, revisiting the simulation model and proposed to drill a horizontal infill production well instead of dual-string deviated well (originally envisioned for long string as an infill producer in the target reservoir and short string completed as a water injector in a different reservoir). Due to further well performance analysis, injection support on the other reservoir was no longer deemed necessary. The team analysis for arriving at this optimization as well as the behind-casing recompletion opportunities will be shared in this paper. This is a case study on how assimilated and innovative approaches undertaken in a different economic climate can be crucial to accelerate a brownfield re-development to extend the economic life of a mature producing field. A multi-disciplinary team applied creative thinking and cooperative efforts to a mature field to revive economic viability. Revisiting previously identified opportunities in the context of a low oil price environment and a simplified regulatory governance process, cross functional and cooperative technical and financial team work were important in facilitating timely decisions needed to move forward. A similar approach will be undertaken for future work in other fields.

Major reservoirs in Field A namely A-2, A-3U, A-3M, and A-3L, are deposited within a multi stacked channel complex system within Group I in Malay Basin. These reservoirs were previously understood based on existing data to have no or very minimal vertical communication between them and are treated as separate systems. In 2018, three wells were proposed to drain the attic oil in the north region of A-3U reservoir. When drilling these infill wells, it was discovered that the pressure has exceeded initial reservoir pressure although the reservoir has been idle for almost a year prior to the drilling. The results of the multi-rate test of two of the three infill wells that are less than 1 km apart are significantly different from one another. Post drilling, more tests were executed to investigate the connection between the sand. Studies were also done by incorporating the static and dynamic reservoir modeling data. Based on the result of the tests and studies, it was concluded that all of the major sands are connected at some areas. This new finding on the connectivity might be able to explain the additional volume needed to history match some of the reservoirs. Establishing stratigraphy concepts of a reservoir particularly in a channel complex system is an ongoing process, in this case, a brown field of almost 20 years of production. All data including new well data and dynamic data plays a vital role for a better understanding of the reservoir. It is essential to incorporate the updated geological understanding into the static model to have a representative simulation for better history matching and prediction. Moving forward, instead of building a separate grid model for each reservoir, a larger framework consist of intercalated reservoir grids will be built with this new geological understanding for dynamic simulation.

S Field field started enhancement planning and redevelopment recently by using an innovation EOR program called GASWAG, Gravity Assisted Simultaneous Water and Gas, in the selective oil-bearing sands. The initial program includes 6 infill producers, 2 water injectors, 3 gas injector wells and approximately 15 potential well reactivations to increase recovery by 7%. Since GASWAG is a new program in this region, it requires well and reservoir monitoring system to be implemented to have better understanding of complex behavior of water and gas injection and its effect on EOR performance. The main objective of the EOR Integrated Operation (IO) workflows solution, is to determine as quickly as possible if EOR performance is deviating from plan. This will be accomplished by earlier detection of EOR performance exceptions (compared to process without IO functionality), so that corrective action cycle time can be reduced, thereby reducing production deferment. Well Surveillance & Operational: a workflow to monitor, analyse and manage EOR wells production/ injection performance using real-time and in-time data together with updated well model. This Workflow focus on well and zones monitoring by using the well model and existing measurement. In addition, the existing IO workflows are integrated with EOR-Operational and feeding online data to this WF which is consistent with operational safety limit and KPOs. All operational data required for reservoir and production engineering were extracted either from well model, measurement or other workflows to the same well interface. Additionally, production and injection well surveillance and alarming system is implemented to benchmark the current operational condition deviated from plan or operational limit. Updated dynamic model and optimizer tool are used to define the optimum choke size of each reservoir layer for injecting or producing wells. This workflow was built and implemented successfully. It is designed based on very comprehensive technical aspects and KPIs from reservoir management, production engineering, facility constraint, well integrity to operational optimization. A single interactive visualization interface via web-based is implemented which cover all necessary production and reservoir data needed for collaborative decision making. The EOR well surveillance IO workflows will assist in automating computation of injection and production well health and performance. This solution benefits the asset team by allowing early detection of underperforming injection and production wells. Main challenges in S Field was, it is divided by several jackets thus require movement via vessel for manual data gathering. Unpredictable and adverse weather heavily challenge this activity. By having IO would help to improve data hygiene and collective data on daily monitoring. Additional functionality of the well surveillance workflow includes the monitoring of zonal rate and pressure, which are considered as main reservoir performance parameters. Operations, production, and reservoir engineers, as well as technical & business owners benefit from these workflows to steer the EOR operation.

The digital transformation journey provides new opportunities for running simulations through cloud computing, with flexibility in hardware resources and availability of a wide array of software tools that can enhance decision-making. This work reviews the benefits of running reservoir simulations in a cloud environment and demonstrates the efficiency and cost savings. Additionally, a workflow for uncertainty analysis and history matching that integrates data analysis and machine-learning tools is presented. First, the hardware architecture must be designed to meet parallel reservoir simulation needs: significant message passing occurs between computer nodes, and for satisfactory performance, these nodes must be connected by a low-latency network, rather than be randomly located. Second, to ensure portability and easy replication across multiple cloud sites and platforms, the software performing the simulations must be containerized. Third, to reduce the time required to start a new simulation run, the Kubernetes platform is used to optimize resource allocation. Finally, reservoir simulation in the cloud is no longer merely the running of the simulation model, but it is integrated with data management and data analysis tools for decision-making. The cloud-based simulation services discussed herein exhibit good results during scale up, when a simulation operation requires a larger number of central processing units and/or greater memory, and also during scale out, when thousands of operation scenarios are necessary for history matching. The "pay as you go" pricing model reduces the time and capital costs of acquiring the new computing infrastructure to nearly zero, and the effectively unlimited scale-out capability can reduce the elapsed time for history matching by 80%. The availability of data centers in different regions is good for team collaborations. It serves the data management tool well to track history data, perform data mining, extract more information, and make decisions. Compared to traditional reservoir simulation, the cloud-based reservoir simulation software as a service model simplifies the process and reduces hardware acquisition and maintenance costs. Integrating intelligent data analysis with simulation helps quantify the uncertainty in the model and enables improved decisions.

This paper highlights a systematic and robust computer assisted reservoir simulation workflow to place oil producers and water injectors in the optimum areas of hydrocarbon reservoirs. The workflow offers modeling of several types of well pattern designs, including five-spot, fork-type, line-drive, and peripheral injection, allowing the multidisciplinary subsurface team to evaluate several development plans very efficiently. The workflow relies on automating the process of well placement across areas of interest with integration of static geological and dynamic simulation models. Opportunities within the reservoir are targeted using different well types, including vertical, horizontal, and multilaterals. This process incorporates different elements of well pattern designs, including well spacing, lateral length, azimuth, dogleg severity (DLS), and completions. For example, star-shaped offshore platforms with multiple wells are placed parallel to structure contours of the reservoir honoring oil-water contact (OWC) and gas-oil contact (GOC). Multiple zone lateral placement is achieved by positioning each lateral in targeted reservoir layers. This workflow was evaluated on a conceptual tight heterogeneous reservoir with several development strategies, including star-shaped platforms with peripheral water injection. Each platform consisted of several dual and trilaterals targeting different horizons of the reservoir. The development plans were evaluated through numerical simulation and well performance was analyzed. The analysis indicated considerable improvement in field cumulative oil production and ultimate recovery. Different field development plans (FDPs) are generated in a few minutes instead of months when using traditional workflows. Overall, a 90% reduction in time has been observed in this work. The significance of this workflow increases when preparing FDPs for geologically heterogeneous reservoirs with complex well designs and challenging operational constraints. The results achieved using this workflow demonstrate a unique opportunity in creating a variety of well designs automatically to target huge and complex reservoirs under a secondary drive production mechanism. This workflow is not only valuable in early phase field development planning, but also applicable in designing multiple sidetracks and reentry wells as part of the infill drilling program in mature fields.

Infill drilling consists of adding horizontal wells between existing wells to optimize drainage in high-value acreage. New wells are sometimes drilled as close as 250 ft to producing or depleted wells. Fracturing the new wells creates a high risk of fracture-driven interactions (FDI). This paper describes a methodology to characterize well interference on production in unconventional basins and the impact of mitigation technologies. Data mining, correlations, and statistical tools were developed to extract and analyze a large commercial production database covering major plays in the US. First, cloud-based algorithms were developed to identify and characterize infill wells based on coordinates, well deviations, production dates, and an adjustable radius of interference. Second, monitoring algorithms automatically captured and analyzed abrupt changes in normalized production of infill wells and neighboring wells at the time of infill well stimulation. Finally, the effect on production of both parent and child is immediately displayed on a user-friendly user interface for further visualization and interpretation. The method was successfully applied to areas experiencing high infill drilling in major basins such as the Williston basin. Results show that production data correlate with historical changes in infill drilling density and fracturing job volumes (proppant and fluid). The production of child wells is then compared to that of their closest parent, which shows some decline as a function of the distance between wells. The systematic workflow also identifies if the basin is prone to positive fracture hits or if there is a significant decrease in the production of existing (parent) wells. The use and impact of diversion technologies as a well interference mitigation method is also studied. These results give important insights into the effect of field development strategies on well interference and enable recommendations related to well spacing, fracturing designs, and use of fracture geometry control technologies to optimize future well and field development. Production data analysis clearly shows a beneficial impact of both near-wellbore and far-field diversion technologies on production.

In today's fast paced and challenging oil industry, the need of faster evaluation studies for quick generation of field development plan (FDP) is becoming more crucial to remain competitive. Field's geological and structural complexity, uncertainty of production data adds to the challenges. Traditional approach of building dynamic mesh models carrying out numerical simulation to history match, then predict has always remained time consuming in large mature fields. The ‘B’ field in Peninsular Malaysia is a mature clastic with stacked reservoirs having a huge gas cap with moderate aquifer. Significant production over last 30+ years led to uneven movement of the gas cap and also of the edge aquifer leading to possibility of bypassed oil. The updated dynamic model could not match the preferential gas cap movement, thus failed to match the high GOR of downdip wells and also unable to match high watercut of certain updip wells. To identify the areas of bypassed oil thus is a significant challenge with the current dynamic model. New engineering tools of polygon balancing, material balance, normalized EUR bubbles were used with the 3D static model volume and the facies understanding. The uncertainties and risks were also identified and clear measurable methods were proposed to address the uncertainties and reduce the risks. Very detailed decision tree with clear data gathering plan to drill successive optimum wells have been planned during the campaign. This paper details the new engineering tools used to delineate and quantify the bypassed oil in these huge clastic reservoir with preferential gas and water movement, unable to be history matched by the dynamic model. It explains the engineering methods applied to identify and quantify the 10 infill wells proposed for the development campaign. To reduce risks, this paper would also explain the blind testing that was carried out on for this new reservoir engineering analysis tool by deriving the infill potentials of the previous campaign (4 years back) by the same method. The paper details how robust technical development plans were generated having infill well locations and reserve determination. This paper will also demonstrate the classic "Do-Learn-Adapt" strategy through its infill wells prioritization & ranking, subsurface de-risking analysis, data acquisition and mitigations plans.

D-X oilfield implemented an infill adjustment from 2013 to 2015, adding 101 development wells, two central processing platforms and two wellhead platforms. After the adjustment, the oil field greatly improved the engineering processing capacity, but it had also entered a period of rapid decline. In order to mitigate the decline and improve the recovery rate, 65 adjustment wells were implemented in this oilfield after the first adjustment. Based on the new drilling data and production data, earlier inefficient well management was turned a more efficient well management, where the engineering processing space is fully utilized, the decline of oil field is alleviated, and the development effect is greatly improved. This this paper expounds how to excavate the potential again after the redevelopment project in large fluvial facies oilfield in Bohai bay, taking this oilfield as an example.

Since late 2017, the Haynesville Shale has seen an uptick in activity as more operators have started to drill more new shale wells than at any other time since the industry was slowed due to declining oil price at the end of 2014. Some of the new activity has been focused on pushing the economic boundaries of the Haynesville shale out whereas others have focused on drilling infill wells or wells that are drilled between pre-existing wells (known as "parent wells"). Parent wells may cause pressure depletion in the reservoir, potentially hindering the performance of new infill wells. The distance from the parent well to an infill well along with the degree of reservoir depletion caused by the parent well impact, to varying degrees, the production results of the infill wells. It is important to design a completion program in the infill well that minimizes the potential negative impact of depletion. This paper presents detailed studies assessing the impact of the change in offset well spacing and reservoir depletion related to parent wells on infill well performance through modeling in the Haynesville shale. An actual reservoir dataset was utilized in the Haynesville shale to build the parent well hydraulic fracture and reservoir simulation models to account for fracture calibration and production history matching. The models' results were then used to evaluate the impact of production depletion on the stress reorientation and changes in stress magnitude through a coupled boundary element and finite element model residing in a geomechanics simulator. Three different production depletion times were modeled through the simulation, 0.5, 1, and 3 years, to understand the timing impact on the infill well production. After the stress in the model was updated for each case, a child well pad was added to the model adjacent to the parent well. The well spacing, stimulation job treatment, and fracture stimulation pump rates were all varied for child well simulation and evaluated to understand their impacts on the created complex fracture propagation and total system hydrocarbon recovery. In this study, more than 200 different scenarios were simulated by using cloud computation, and each parameter was compared for varying spacing scenarios for the three depletion time horizons. This study can help the understanding of well spacing, completion job design, and reservoir depletion impact on the new infill well performance and help the optimization of the infill well completion strategy to achieve optimum production performance for new infill wells and minimize communication or fracture hits to the existing parent wells in the Haynesville.

The objective of this paper is to demonstrate the process of unleashing the potential of digital oil fields by combining the power of Big Data platform with the Internet of Things (IoT). This new method enables efficient machine learning training utilizing Big Data and real-time scoring against mathematical models for predicting future outcomes. Digital oil fields have a diverse set of IoT devices that measure important field metrics in real-time, such as downhole pressure, temperature and oil rate. A typical digital oil well is equipped with many equipment such as Multiphase Flow Meter, Electrical Submersible Pump, and Permanent Downhole Monitoring Systems. Those equipments have several sensors generating a huge volume of data every second. In order to enable data scientists to analyze this huge amount of data streaming from various data sources, a data engineering pipeline was built. This pipeline combines data from various real-time and historical data repositories along with a master relational database in order to provide a consistent and clean analytics database for data scientists. This method saves data scientists the trouble of manually preparing and cleaning data from different datasets. Furthermore, by utilizing the analytics database cluster for machine learning, data scientists were able to use bigger data sets for training their models which can improve the accuracies of the models. As part of the solution, a scoring engine was built which consumes real-time data feed from the digital oil fields and performs real-time predictions and scoring utilizing machine learning models. The new architecture significantly improved the productivity of data scientists by allowing them to focus on building models and not to have to worry about data plumbing and deployment of the model to the field. Moreover by utilizing bigger data sets, models accuracies was improved considerably. Finally by integrating the models with IoT real-time data stream, field engineers can see and act on the models’ predictions in a timely manner. This architecture and methodology combines different technology domains (IoT and Big Data) with unique solution to bring value to the Oil and Gas producing & production business function.

‘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.

This paper discusses an alternative study approach with multiple lessons learned from a recent successful infill drilling campaign in a medium-sized brownfield. The team conducted a practical and an optimized subsurface study, including the no-frills classical reservoir engineering and a simple sector model to justify the infill wells. From idea generation to monetization of the barrels, it was fast and cost effective without compromising technical assurance. Results of the infill drilling campaign and lessons learned are discussed in this paper. This is a case study on how a comprehensive understanding of reservoir complexity using creative data integration can be an adequate tool for field development. The results of the campaign demonstrated that an optimized study helps the operator and partners make an efficient investment decision in materializing development opportunities.

Located offshore East Malaysia, S-C Field is considered a marginal oil field with first development in 2008. While it is a shallow marine with storm and wave influence setting, the reservoir sealing is provided by the unconformity and Major Fault in the west side and further split into three major sub-blocks namely North, Middle and South Block. Middle Block carries two third of total field production as it also carries 80% of total oil volume making it the biggest producing block in S-C field. The field separation into sub-block came as a surprise during the development drilling which has totally impacted the production strategy and oil recovery as the field is initially expected to be a single buried hill with two separate major oil accumulations. Sand discontinuity and multi-stacked nature of the reservoir proved to be major challenges during development drilling and production phase. Despite the successful drilling and production from most of the development wells, excessive sand production lurks as major threat to oil production and well life. Careful implementation of drawdown limit and sand count monitoring was implemented, nonetheless the oil recovery remained low and the need for additional drainage points was assessed to unleash the full field potential. This paper captured the case study on this marginal oil field redevelopment which has substantially revived its oil production and successfully overcame the production challenges despite the geological surprises, lesser in-place volume, unoptimised completion and forecasted lower recovery compared to initially prognosed. From the early identification of potential additional infill candidates, project acceleration and economic improvement, the post-production performance has indicated a huge transformation towards realising more oil incremental at reduced cost. Additionally, there were significant lessons learned on subsurface planning and project execution. These were then highlighted for way forwards plan for S-C field and S Cluster in general to further sustain production and extend the field life.

Development drilling in the Prelude field was successfully completed in 2015. The development plan included seven horizontal wells to deliver high reliability production to the Prelude FLNG facility. The Prelude FLNG development in Australia will be the first deployment of Shell's Floating LNG technology. There were a number of challenges associated with the Prelude field development plan. These included drilling the first horizontal wells in the basin, the need to meet FLNG operating constraints, and management of reservoir uncertainty. The challenges needed to be considered in the context of a limited intervention strategy, hence the need to "get it right the first time". Challenges were addressed in three project phases. Firstly by thorough field development planning in the Concept Selection phase of the project, leading to a development concept that was robust across a range of reservoir uncertainties and risks. Next was extensive contingency planning in the pre-drill phase to enable structured and timely decision making to manage a range of subsurface outcomes and operational events. Finally during the execution phase, drilling was separated into two seasons to allow learnings from season 1 to be incorporated into season 2. The success of this approach was enabled by a fully integrated multidisciplinary team of Subsurface and Well Engineering staff supported by global expertise from within the Shell group.

The Nong Yao field is operated by Mubadala Petroleum on behalf of the G11/48 block concessionaires with KrisEnergy and Palang Sophon Limited. Nong Yao recently commenced production following a successful development drilling campaign, which was extremely challenging due to subsurface uncertainties. The subsurface team adopted an innovative method of well sequencing and optimization of targets, such that every well drilled is used to de-risk other wells, in order to avoid costly additional appraisal drilling. The key methodology involved a deep understanding of the exploration and appraisal data gaps and as a result the uncertainties / limitations of the static and dynamic models. A field development plan was developed that could achieve additional appraisal objectives, and in doing so, de-risk other wells as the development was executed. Uncertainties that the team sought to mitigate included structural uncertainty (due to shallow gas effects), fluid contacts and fluid type uncertainty, sand distribution and connectivity uncertainty and also uncertainty in the aquifer extent and degree of pressure support expected. This information was gathered by planning deeper high deviation development wells with complex 3D trajectories, that could intersect multiple reservoir sands and provide the formation evaluation and well landing points for later horizontal development wells in shallower reservoirs. Achieving appraisal objectives while drilling both in the static and dynamic sense, helped in optimizing well locations and led to the cancellation of multiple water injection wells, which were not required as drilling indicated better aquifer and pressure support than initially expected. This led to substantial savings in well costs and enabled rig slots to be utilized for production wells rather than unnecessary injection wells. Key technologies were used to achieve appraisal objectives. A high build rate hybrid RSS tool was used to deliver complex 3D well profiles and land wells above oil-water contacts while maintaining high ROP and wellbore quality. The deep resistivity distance to boundary LWD tool ensured horizontal production wells stayed in the reservoir sands and also helped to map the top and extent of structures, improving reserve calculations and reservoir simulation. In key wells the use of ultra-deep resistivity for reservoir mapping combined with the LWD near-bit triple combo, helped in mapping the reservoir prior to entering it, eliminating the requirement for separate pilot wells. Innovative formation pressure acquisition methods were used to better understand reservoir connectivity and depletion and to determine whether injection wells were required. It also helped to reduce uncertainty on fluid contacts, providing better accuracy and optimizing well positioning. The impact was that more marginal oil pools could be developed with a higher degree of confidence. The clear value of these innovations was a reduced overall development cost and wells better placed for recovery and production. With lower development costs, more reservoirs of this nature in the Gulf of Thailand can become viable to develop, which has a significant impact on the future of Thailand's oil and gas industry.

The digital oilfield is a fundamental consumer of information technology. It requires the sourcing of reliable data, managing and storing that data effectively and serving it to operational decision makers. Cloud computing has forever changed the game in information technology. It could bring significant enhancements to the digital oilfield providing that the industry feels comfortable that data resilience, privacy and security are maintained. This paper looks at how to provide this assurance. The big concerns with cloud computing are data loss and an uncertainty where the data is actually residing. Coupled with a growing number of security threats, these are holding back adoption in digital oilfield implementations. Some companies have introduced a "private cloud", which has enabled them to retain data within their own network boundary. However, this has prevented them from reducing their infrastructure costs and they have had to size their computing power and storage requirements to meet the highest level of demand, which may only be needed occasionally. Conversely, a public cloud enables a "pay as needed" service. The cloud benefit is not just concerned with the cost of information technology. Smart applications can be deployed quickly to aid decision making, access to information is improved enormously and systems of record are much easier to manage. These processes support the very foundations of digital oilfield: operational efficiency in drilling and production, enhanced collaboration and remote decision support. So we are not just making an information technology decision when considering cloud technology. In this paper we demonstrate that the cloud can not only be as private and secure as a ring-fenced infrastructure but in many cases it offers much higher levels of protection, resiliency and agility. As with many aspects of the digital oilfield, the challenges to success reside in people transition and the development of appropriate work processes. We have a natural tendency to over-estimate the privacy and security of our own infrastructures but unless they are actively rather than passively managed, it is most unlikely that the governance processes will be superior to those employed by Microsoft in their Azure Cloud, for example. The digital oilfield is a journey towards advanced ways of working. The oil and gas business is evolving continuously as we develop ever more challenging fields. In the future we can expect to have more limited resources available and possibly, as we are seeing currently, lower returns. The cloud is an important evolution that we cannot afford to overlook.