Kuwait Oil Company (KOC) is operating two Heavy Oil fields. Field A aims at production by Cyclic Steam Stimulation (CSS), followed by steam flood. Field B envisages primary recovery through cold production, followed by non-thermal Enhanced Oil Recovery (EOR). This requires drilling and completion of large number of wells. Implementing Well, Reservoir and Facilities Management (WRFM) and Smart Field approach will be a key requirement for operation excellence in these fields. Currently both fields have some wells in production, mostly as single isolated wells or wells in 5-acre/ 10-acre spacing. These pilot projects aimed at de-risking the commercial phase, which is to follow in the coming years. These wells are the training ground for young KOC staff to learn how to work in integrated teams using WRFM processes. WRFM processes are tailor-made for KOC's operating environment. These processes include Digital Oil Field based on Exception Based Surveillance (to flag out only those wells and facilities outside of their operating envelope and/or optimization window) and Production System Optimization. This would help to eliminate operational bottlenecks, leading to optimization in manpower to deal with large number of wells. It is expected to be achieved by combining existing best practices of International Oil Companies (IOC) with existing KOC applications, leveraging successful global practices. The paper shall highlight the timeline, activities and organizational changes underway to effect the transformation from existing operation to a larger and more complex development that includes continuous drilling, completion and well intervention (CWI) and facilities installation occurring simultaneously. The implementation of WRFM Processes along with Digital field will achieve the production and operation goals by reducing well, artificial lift, and facility downtime. This innovative production optimization system by enabling efficient decision-making process shall lower the cost per bbl. and reduce down time by implementing automated surveillance workflow.

As KOC prepares for production ramp up in a North Kuwait Heavy Oil field, one key for cost efficient development is in optimizing information from observation wells. Given multiple objectives for data collection and restrictions due to dense well spacing and surface infrastructure, it is critical to apply an efficient methodology for the selection of observation well locations. This paper describes the interplay between subsurface needs and urban planning in decisions for placement of observation wells to reduce operational costs, enforce effective field development planning and ensure robust production levels. The observation well location selection protocol in a heavy-oil steam development requires initial focus on identifying the key subsurface parameters and risks including cap-rock integrity, role of baffles in steam conformance, gas caps, aquifer influx as well as development decisions like adequate well spacing to apply micro-seismic, e.g. Key parameters were mapped and rated with a "traffic-light" approach to distill the diverse datasets into manageable form. Next, different observation well location strategies (evenly distributed, clustered and blended) were assessed and locations (driven by subsurface objectives) were chosen. Finally these locations were reconciled against the surface restrictions, HSSE and operational constraints to determine the final program. A successful observation well strategy will have: – Good subsurface models capturing heterogeneities in order to predict production performance – Sufficient well number and thorough reservoir architecture characterization underpinning well placement – Robust data collection plan (including production data) – Proper staff resources applied to monitor and QC the data – Up-to-date and accessible data-base, that can be interactively interrogated – Cross-discipline integration, regular data reviews and alignment with urban planning The inherent value in observation wells is optimized by defining key objectives with broad across discipline engagement, aligning data collection objectives and engaging urban planning and field development with sufficient lead time. This collaborative approach reduces costs for KOC by avoiding rework, missed data collection opportunities or missed value adds if options such as conversion of observers to producers are not considered.

The first production log ever run in a heavy oil, long horizontal well completed with premium screens in open hole and through a Y-tool was successfully executed by Petroregional del Lago S. A. (a joint venture between PDVSA and Shell) in the Urdaneta West Field (Lake Maracaibo, Western Venezuela). The purpose of the job was to identify the origin of water in a well that experienced water break-through from the first day of production. A specialized set of logging tools was run to detect both water and oil flow, as well as to determine any flow behaviors like cross-flow that would help understand the source of water in the formation, obtain sufficient data to prepare a water shut-off program, and establish basic productivity information from the well, being this also the first production log run in the heavy oil wells in the field, which require artificial lift to flow. The results of the production log indicate that a sand package is producing water from an unexpected zone, which will require a water shut-off workover. This paper describes the planning, operational and interpretation processes of the logging activity, and presents a number of lessons learnt and useful recommendations for similar activities in heavy oil wells. Introduction Petroregional del Lago S. A. (abbreviated PERLA) is a joint venture between Petroleos de Venezuela S.A. (PDVSA) and Shell International Exploration and Production (60% and 40% share, respectively). The company operates the Urdaneta West Field, located in Lake Maracaibo in west Venezuela. The field has three producing reservoirs, with very different geological characteristics and well types. One of these reservoirs is the Misoa formation, which is a Miocene formation consisting of unconsolidated sands and containing very significant reserves of heavy oil. The main characteristics of the Misoa Reservoir are summarized in Table 1. The well UD-785 (MIB-10) was drilled as a heavy oil producer in the Misoa formation and completed with an Electric Submersible Pump (ESP) completion with a Y-tool. The Premium screens (mesh type construction) were installed in the reservoir section, in open hole. The well was not gravel packed. The final completion diagram is shown in Figure 1. The well was started to production with the ESP, and in the first day of production the water cut stabilized quickly to 98%. The salinity of the produced water was measured and was found to be equal to the known formation water. The well initially produced at a rate of 2000 bbl/day for 5 days, and it was shut in to avoid operational problems associating with handling the produced water at the tank terminal. The initial well test evaluation indicated a big productivity index of 13 bbl/day/psi, one order of magnitude above the expected productivity based on the logs. A down-hole multi-sensor was installed with the ESP assembly, so data was available of the different pump parameters. The pump intake pressure measurements indicated a higher than anticipated reservoir pressure, with a fast build up period after the well shut-in. The geological and petrophysical analysis of the open hole logs obtained with LWD tools indicated that a stratigraphic marker that had never been drilled before in the area had been penetrated. The normal porosity, resistivity and shale content cut-offs used in the reservoir were applied to the logs in this section, indicating the presence of oil, but also of higher that typical water saturation. This sand package became the main suspect for water production. See Figure 2.

Some of the oil fields offshore Brunei are characterized by complex reservoir geology. This requires the drilling of high-complexity, tortuous 3D horizontal wells referred to as "snake wells" for optimal reservoir drainage. These wells deliver an ultimate recovery equivalent to multiple horizontal wells drilled in the same structure. This development concept was chosen as the most beneficial with the business value drivers for the Selangkir Iron Duke (SKID) project. Over a period of several years, drilling performance had improved but plateaued and still contained hours of nonproductive time (NPT), including hole conditioning wiper trips, rough drilling causing bottomhole assembly (BHA) failures due to vibrations, troublesome trips, and even lost production due to stuck-pipe incidents. In previous "snake well" drilling campaigns multiple additions to the BHA design to overcome tight hole problems had seen an ever more complex and rigid BHA being adopted, but without the required NPT or well cost reductions. In an attempt to make a step change in performance the BHA design and drilling processes have undergone a comprehensive revision. The primary focus areas were to enable shoe-to-shoe runs, reduce the risk of stuck pipe, improve tripability of BHA, reduce torque and drag and last but not least improve the overall hole section progress. A more flexible, slimmer rotary steerable system (RSS) BHA design having a shorter, compact sourceless logging-while-drilling (LWD) tool was proposed to reduce damaging vibrations and ease tripping. The paper discusses engineering solutions implemented to mitigate risks associated with complex well geometries which consequently contributed greatly to deliver the high-complexity extended-reach drilling (ERD) wells with top quartile and even best-in-class performance in the Rushmore's Drilling Performance Review (DPR).

The objective of this study was to assess the feasibility of application of analytics techniques in a new heavy oil asset in Kuwait in the following areas: data integration and visualization to support Well, Reservoir and Facility Management (WRFM), understanding well production behavior and their link to reservoir parameters, investigating reasons for sand failure. The study also aimed to highlight focus areas that would facilitate the full field implementation of analytics as a viable WRFM tool. Due to the green field nature, the current volume of data is relatively small (versus mature assets), and not all the data required is available yet. The study started with formatting, processing and integrating all the available field data into a data management tool. Integrated visualizations were tested to detect early trends of sand and water production. Machine Learning algorithms such as Random Forest, Decision Tree and Neural Network were applied next to the sand prediction problem, focusing on identifying root causes for repetitive sand failures in wells and if possible to predict initial or subsequent sand failures. The study indicated that integrated visualizations are very promising in support of WRFM in this field in the short term. Early signs of water breakthrough were correlated, particularly in wells with specific combinations of geological features and completion strategies. The sand prediction problem proved to be very challenging to the Machine Learning approaches, with limited success in predicting the historical occurrences. The results indicated that these techniques are likely to be more applicable once the volume of data increases, particularly the higher resolution data (real time data from artificial lift equipment), as well as by incorporating additional data types (sand production measurements during tests, which require additional resources to execute) and other data types available but not tested yet (log data derived parameters). Overall results indicated that analytics should be strongly considered as a valuable tool in the short to medium term in this new field, with efforts in data acquisition and management of the data types identified. Most of the existing publications on this topic are related to analytics applied in mature fields. This study, conducted in a field still in the early stages, showed the value for early implementation, and highlights that early planning for focused data acquisition can facilitate building initial data-driven models which can be used for prediction purposes. The paper is expected to provide a valuable reference for new heavy oil projects, either under definition or in early production, for the application of data analytics and extend learnings based on machine learning.