The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
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The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
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Al-Samhan, Amina (Kuwait Oil Company, Al-Ahmadi, Kuwait) | Jilani, Syed Zeeshan (Schlumberger, Al-Ahmadi, Kuwait) | Al-Nemran, Shahad (Kuwait Oil Company, Al-Ahmadi, Kuwait) | Muhammad, Yaser (Schlumberger, Al-Ahmadi, Kuwait)
Abstract The Greater Burgan field has been on production for over 75 years mainly from the homogenous massive sands of the Burgan clastic sequence. Given the increasing field water cut from these sands, it is now a matter of strategic focus for the asset to target the generally untapped thin, laminated low quality sands to sustain target production. This paper focuses on a case study for a horizontal well design and completion optimization using sector modeling. An updated dynamic model, covering the area of interest, was developed. This is an extremely important tool to achieve the study objectives. A sector model was cut out from the full field dynamic model. Grid refinement was performed on the sector, in both vertical and horizontal dimensions. Newly drilled wells were used to update the model horizons, petrophysical data from offset wells in the sector, including geosteering data from the pilot hole, were upscaled and properties populated across the model. The dynamic model calibration was conducted successfully by including all available well events, workovers, production data, static and flowing bottom hole and well head pressures including all other surveillance data from offset wells. To better match the historical field pressure and water-production, sensitivities were conducted to determine the model response to various parameters including the aquifer strength and faults conductivity. Adjustment of the aquifer strength enhanced the field pressure match, invariably improving the calibration of the model. After model calibration, the horizontal well was implemented in the model, in line with the design scope from the asset. The biggest uncertainty was the oil-water contact (OWC) in the sector near the planned well. Although offset wells gave a reasonable estimate of the OWC, it was used as sensitivity parameter to cover the uncertainty. This was taken forward into the model prediction simulation work. The modeling study provided immense insights into the probable outcomes in terms of actual horizontal well production deliverability. Multiple rate sensitivities were conducted mimicking the different choke sizes which were planned. These were used as a guide for the asset to set reasonable production target rates for the well. The study also provided a technical justification for completion recommendations and optimization with a view to maximizing the well's production over time. The horizontal well has been drilled, completed, and tested in the field. The production test rates were encouragingly consistent with the model predictions. The workflow methodologies adopted in this work have now been extended to other wells being drilled in the field.
Kurma, Ravi (Kuwait Oil Company) | Al-Fadhli, Wuroud M Kh (Kuwait Oil Company) | Ayyad, Hazim (Schlumberger) | Mehraj, Majid (Schlumberger) | Alajmi, Abdulaziz (Schlumberger)
Abstract It has always been a challenge to accurately detect downhole sand producing zones. Older generation acoustic based sand detecting technologies were subject to error due to background noise related to the fluid production. Due to the common false detections with the legacy measurements, remedial workover plans were compromised (Adil, 2020) A new technology that can differentiate between sand production and background fluid related noise has been developed. This technology can quantify the sand grain count through a piezo-electric sensor. In this paper, a unique comprehensive study was conducted across a sector of the Greater Burgan field to better understand the variables contributing to sand production through integrating the static model along with the dynamic production profile which included the new, state-of-art downhole sand detection technology. Based on the results, a guideline was formulated to predict the future sand producing wells. This helps to proactively anticipate and therefore, better manage the sand production challenges. This paper covers the results of nine wells logged across the same reservoir units. The main objective was to identify and quantify the sand producing intervals. The secondary objective was to determine the water producing zones. Wireline production log were combined with this new technology in all the wells to obtain the fluid production intervals along with the sand producing zones at the same conditions in the same run. Along with the spinner, water hold up probes pinpointed the water entries. The sand impact detection tool provided the fourth phase, sand, with excellent repeatability. Multiple rates were conducted in some of the wells to determine the critical drawdown pressure at which sand production occurs.
Al-Murayri, Mohammed Taha (Kuwait Oil Company) | Al-Qattan, Abrar (Kuwait Oil Company) | Wafaa, Al-Ghanim (Kuwait Oil Company) | Kakade, Shweta (Baker Hughes) | Banerjee, Anirban (Baker Hughes) | Mascagnini, Carlos (Baker Hughes) | Khamatdinov, Rafael (Baker Hughes) | Ali Chughtai, Muhammed (Baker Hughes) | Andrianov, Alexey (ZL EOR Chemicals) | Malcolm, Pitts (Independent)
Abstract The Greater Burgan field in South-East Kuwait is the world's largest sandstone oilfield and the second-largest conventional oilfield. The Wara reservoir, in the Greater Burgan field, is a prolific sandstone oil-producing formation. Peripheral water injection into the Wara reservoir is in progress for pressure maintenance and to improve oil recovery from the flank areas. Polymer injection has also been identified as a practical EOR method that can potentially increase oil production and recovery from the Wara reservoir. In view of that and, as a follow-up to a previous Long-Term Polymer Injectivity Test (LTPIT) (Murayri et al. 2022), a second LTPIT was carried out targeting a different area within the Wara reservoir. This paper describes elements of the polymer injection predictions approach, results obtained from a dynamic simulation sector model, before and after polymer injection, in pursuit of phased commercial polymer-flooding development using fit-for-purpose modularized water treatment and polymer mixing/injection facilities. Prior to the commencement of polymer injection, a representative 3x3 km sector was extracted from the full-field dynamic model. A fine grid numerical simulation model was then history matched and calibrated using production/injection history and Step Rate Test (SRT), Pressure Fall-Off (PFO), and Injection Logging Tool (ILT) and High Precision Temperature-Spectral Noise Logging (HPT-SNL) surveillance data. This model was set for predicting polymer injection rates to ensure injection under matrix conditions, at different polymer concentrations, to guide field implementation over a period of 3 months. Pre-LTPIT modeling results demonstrated that injection at commercial rates of >2,000 bpd is possible with polymer concentrations ranging from 1,500 to 1,800 ppm in accordance with the targeted in-situ polymer solution viscosity. During LTPIT field implementation, downhole pressure and temperature were monitored real-time in addition to wellhead pressure, injected polymer solution viscosity and injection rates to evaluate performance and update the sector model. Thereafter, reservoir simulation sensitivity runs were extensively investigated to design an optimal phased commercial development plan. This plan was developed by optimizing well requirements, injected polymer Pore Volume (PV) and concentration. A polymer PV of 0.8 and a concentration of 1,800 ppm were recommended accordingly in conjunction with 40 acre inverted 5-spot patterns. Economic evaluation was performed while considering water-flooding performance as a baseline. The incremental benefits associated with oil production gains and reduced water handling requirements were evaluated against the envisioned investment in additional wells and polymer injection. The optimal case showed an incremental oil recovery factor of 7% over a period of 10 years. This paper presents a case study wherein fit-for-purpose reservoir modelling is integrated with LTPIT surveillance/monitoring data to maximize the techno-economic benefits of phased commercial polymer-flooding in the Wara reservoir of the Greater Burgan field.
Alkandari, Shaikha (Kuwait Oil Company) | Al-Zankawi, Omran (Kuwait Oil Company) | Alsaleh, Zainab (Kuwait Oil Company) | Jaafar, Dunya (Kuwait Oil Company) | Jasem, Mariam (Kuwait Oil Company) | Mudvakkat, Anandan (Kuwait Oil Company) | Khan, Junaid (Kuwait Oil Company) | Alaassar, Ahmad (Kuwait Oil Company) | Perez, Godo (British Petroleum Company) | Rjabli, Nuran (British Petroleum Company)
Abstract As oil fields mature, producing more oil out of maturing reservoirs entails more water production. It is essential to have a successful water management process to be introduce, one that can handle substantial volumes of water produced in order to sustain crude oil production. At the early stage of the Greater Burgan Field water management was not a major concern. Most of its crude oil was dry and easy production with little water from Burgan & Wara reservoirs in Greater Burgan Field. With maturing of the field, South & East Kuwait Asset has started experiencing an increase in water production from its maturing reservoirs. Also starting water flooding in Wara reservoir to increase oil recovery increased water cut with oil production as expected. These changes made the production facilities to become constrained that cannot fully handle produced water volume and result in production deferral. To mitigate the high water cut challenges, South East Kuwait set up a multidisciplinary team to review and come up with actions to tackle the produced water handling challenge. The team looked at an operational initiative to maintain the long-term disposal option of disposing produced water into Shuaiba formation. Shuaiba formation is below Burgan reservoir. During drilling the drilling team encountered heavy fluid losses in Shuaiba. This experience led the study team to support the option of disposing effluent water into fractured Shuaiba carbonate. Shuaiba formation is classify as carbonate formation, full of vugs and fractures resulted from dissolution due to ancient underground water movement, the dissolution led to collapse And creation of large vugs and fractures around the collapsed area. The collapsed area and fractures are the main mechanism for storage of effluent water and increasing disposal capacity for the production facility. The plan is to drill Horizontal disposal wells targeting the Karst to intercept those vugs and fractures. The team saw a promising option to drill in Shuaiba, to increase the chance of loss fluid circulation and enhance facility capacity. After drilling few wells and encountering total loss in the horizontal fractured section, along the edge of the karst, and performing injectivity disposing test, the wells showed ability to take up high water rates more than (50,000 BWPD). Based on the success of these disposal wells, Production operations bottlenecks are resolved in disposal well capacity and increasing it to more than (500,000 BWPD). Disposing in Shuaiba, formation will not require any treatment facilities and is an environmentally friendly long-term option
Al-Zankawi, Omran (Kuwait Oil Company) | Jafaar, Dunya (Kuwait Oil Company) | Al-Kandari, Shaikha (Kuwait Oil Company) | Yaser, Muhammad (Schlumberger) | Prakash, Roshan (Schlumberger)
Abstract In Greater Burgan Field, Burgan formation is the main oil producing reservoir since the start of oil production and with the reservoir mostly depleted, the focus in the asset team is now on the lower quality Upper Burgan (BGSU) sands with substantial remaining oil. In view of this, a multidisciplinary team is formed to evaluate opportunities to stimulate the BGSU reservoir, establish and sustain production from this reservoir to meet the production targets. The objective is to unlock potential in these tight sands especially targeting to revive old wells back which have drained the underlaying BGSM sands and have significant opportunities in the BGSU. The best way to exploit the resource potential of BGSU for its remaining oil is through hydraulic fracturing technology with an emphasis of its implementation via a thorough review of reservoir, well and completion data maximizing wellbore utilization and minimizing CAPEX investment. The review involves screening a batch of wells with low rock quality and low permeability, away from heavy oil zones and above bubble point pressure, with good shale barrier beneath to avoid fracture propagating into the underlying aquifer. In total, about 100 wells are reviewed from across the Greater Burgan Field for this exercise. Latest reservoir pressure and oil saturation maps for the target BGSU reservoir, built in-house by the asset technical team, are being used to identify wells for potential hydraulic fracturing implementation. Upon a review of historical Proppant fracturing jobs that were performed in five wells in 2015-16 in the BGSU sands, it was summarized that there was a substantial increment to the well productivity, in some cases up to four folds. After the review, detailed numerical well modeling of these five wells is performed with and without fracturing showed that well productivity increase by 2 to 3 times for most of the wells. Although the well productivity improved post stimulation, the wells had high drawdown pressures which resulted in early water breakthrough in few wells. An optimum well productivity can be achieved with large proppant volume placement in the reservoir to connect with the sands away from the wellbore. However, with large volumes there is significant risk of frac propagating "out of zone" increasing the risk of early water breakthrough. Presence of a shale barrier above and below the target zone is an important criterion in candidate selection and design the proppant volume to keep the fracture growth within the target zones and not reach to the zones, especially the underlying BGSM formation, having water. With the lessons-learnt from the past fracturing jobs, detailed modeling and simulation of well performance, a refined list of potential hydraulic fracturing candidates can be generated combined with advancements in the fracturing technology which will greatly enhance the success probability of the stimulation with maximizing well productivity. The successful implementation in the candidate wells will lead to unlocking oil reserve in low rock quality reservoir allowing the development of these low permeability sands for the BGSU upper which will help to achieve the aggressive production target of the South & East Kuwait (SEK) asset.
Bodoor, Baroon (Kuwait Oil Company, Kuwait) | Jesudian, David Nelson (Kuwait Oil Company, Kuwait) | Al-Otaibi, Basel (Kuwait Oil Company, Kuwait) | Ibrahim, Hossam (Kuwait Oil Company, Kuwait) | Chao, Chen (Kuwait Oil Company, Kuwait) | Le, Viet (Baker Hughes, Kuwait) | Ghruda, Ahmed (Baker Hughes, Kuwait) | Sofiance, _ (Baker Hughes, Kuwait)
Abstract The Burgan reservoir is located in North Kuwait was discovered in 1957. First commercial oil production from this multistack sandstone reservoir started back in 1964. Operating under active bottom water drive as well as edge water drive. The primary reservoir in the field is Burgan of Lower Cretaceous age, which contributes the major oil production of the fields. Burgan reservoir is the second largest reservoir in terms of STOOIP and reserves, producing 30% of daily oil production. Significant amount of historical production came from the lower part of the reservoir where the pattern of water influx is well understood. Most of the historical production and the majority of remaining reserves are in the upper part of the reservoir where the pressure support and water influx are relatively well understood. Presence of tar was initially detected from cores and produced oil in some of the wells and visual inspection of core material. Tar zone in the Burgan was considered to be structurally controlled and occurred in specific depth window around oil-water contact. Heavy oil has been discovered in basal parts of Burgan sandstone in some of the zones within North Kuwait. Development of Burgan in north Kuwait began in the 1950s with oil recovery since then from Burgan channel sands with few exceptions in the North Kuwait as samples confirmed ultra-heavy oil.
Al-Faresi, Fahad (Kuwait Oil Company) | Monfared, Hashem (Schlumberger) | Evro, Solomon (Kuwait Oil Company) | Ali, Sher (Kuwait Oil Company) | Madhavan, Sethu (Schlumberger) | Yaser, Muhammad (Kuwait Oil Company) | Yunusov, Manuchehr (Kuwait Oil Company)
Abstract Burgan Minagish (BGMN) reservoir in the Greater Burgan field, is one of the reservoirs among others that has been producing from Minagish formation. It has been in production intermittently since 1960's though full development was delayed due to high H2S content and the need to construct sour processing surface facilities. The analysis of reservoir pressure over time indicated that the main reason of reservoir pressure decline in BGMN was production from three neighboring reservoirs producing from the same formation. This was clear as pressure decline continued for the period of 2005-2009 while Burgan-Minagish was not in production. Reservoir pressure dropped below bubble point in 2011 and made it necessary to design and implement a pressure maintenance program. This included a water injection scheme which has been implemented since 2018. In 2012, using a novel approach (Al-Faresi et al, 2012), two Fast and Slow Loop simulation models were built to model a regional aquifer and quantify flux exchange between Burgan-Minagish, regional aquifer and surrounding reservoirs. While the Fast Loop coarse model was used to match field level production and pressure data, the detailed BGMN Slow Loop model would solely focus on modeling of BGMN having incorporated the impact of neighboring reservoirs via calculated flux derived from Fast Loop model. The drawback, however, is that every time the model needs updating, a tedious and manual process must be performed to calculate flux rate across the BGMN boundaries from the Fast Loop and then incorporate it explicitly into the Slow Loop model. 10 years after the previous study, a new approach for regional aquifer modeling has been utilized to integrate two existing simulation models in a combined one that serves for both purpose of modeling the regional aquifer and surrounding reservoirs as well as history matching of high resolution BGMN model. The history matched combined model has been used to extensively evaluate BGMN reservoir production performance under different development scenarios. We have compared the results with existing two-model solution, and it is clear from the analysis that the impact of neighboring reservoirs as well as the regional aquifer is captured in the new approach. The novelty of the new approach is that it allows a seamless interaction between three surrounding reservoirs with BGMN. The unified simulation model is more convenient to use and eliminates the manual work of influx calculations. It is also more flexible and repeatable as systematic updates on both static and dynamic modeling sides for any reservoir in the area of interest could be easily implemented.
Al-Murayri, Mohammed Taha (Kuwait Oil Company, KOC) | Hayat, Laila (Kuwait Oil Company, KOC) | Al-Qattan, Abrar (Kuwait Oil Company, KOC) | Al-Kharji, Anfal (Kuwait Oil Company, KOC) | Bouillot, Jerome (Poweltec) | Omonte, German (Poweltec) | Salehi, Nazanin (Poweltec) | Zaitoun, Alain (Poweltec)
Abstract Improving water-flood efficiency in heterogeneous reservoirs with high permeability contrast is of high strategic importance to maximize oil gains, debottleneck production facilities and alleviate water-handling constraints. This paper presents key lab, simulation and field design insights to implement Deep Reservoir Conformance Control (DRCC) in the Wara formation of the Greater Burgan Field. Prior technical assessment and high-resolution streamline modelling are covered in other technical publications. Full-field high-resolution streamline reservoir simulations have been used to identify 23 candidate injectors for DRCC. The wells having one layer taking more than 50% of the total water injected were considered as good candidates for DRCC to mitigate water channeling challenges and increase sweep efficiency accordingly. Mechanical water shut-off options were considered, but it was confirmed that near-wellbore solutions do not adequately address deep reservoir conformance issues and can compromise water accessibility to unswept oil zones. Furthermore, mechanical water shut-off options require recompletion and can be expensive and difficult to deploy. To overcome these drawbacks, DRCC has been evaluated in an integrated laboratory and simulation study to design a field implementation plan. The recommended DRCC approach involves injecting a microgel followed by a gel. The microgel enables deep treatment while the gel strengthen Permeability Reduction near the well. Laboratory evaluation qualified a microgel having a size of around 2 µm and a gel combining water-soluble polymer with an organic crosslinker. Gelation time was 2 days and full gel consistency was obtained after two weeks, under the form of a strong and slightly deformable gel (E-F on Sydansk scale). Permeability reduction post gelation was as high as 10,000 times. Reservoir simulations were executed to validate this approach, size-up the treatment and forecast performance. A pattern involving an injector and a producer well was selected. Laboratory coreflood data were used as input for the simulations. The combination of microgel followed by gel with a total volume of around 6000 bbl, pumped in two days, induces a gain in oil production of around 20 to 50% in 10 years. Simulation shows improvement of both vertical and areal sweep efficiency. Moreover, the gain appears very early after chemical injection. The combination of microgel and gel gives an efficient in-depth conformance system that can increase waterflood efficiency in formations such as Wara. This innovative approach has high potential in multi-layer high-permeability heterogeneous sandstone reservoirs.
Dashti, Qasem (Kuwait Oil Company) | Abdulrazzaq, Hanan (Kuwait Oil Company) | Al-Shammari, Nouf (Kuwait Oil Company) | Al-Habashi, Haytham (Kuwait Oil Company) | Al-Rumaidhi, Meshal (Kuwait Oil Company) | Franco, Francy (Schlumberger) | Muhammad, Yaser (Schlumberger) | Prakash, Roshan (Schlumberger) | Iheanaetu, Samuel (Schlumberger)
KOC-SEK asset has the main goal of maintaining, increasing, and optimizing the production of the Greater Burgan, the largest clastic reservoir in the World. Discovered in 1938, Greater Burgan is a multilayer formation with large hydrocarbon volumes of different oil type (from heavy to light) for which a detailed characterization and exploitation strategy was required to develop a sustained asset production. With the asset facing multiple challenges to maximize recovery and sustain target production field rate, one of these challenges has been to optimize the segregation of a large number of wells into 15 gathering centres. This is to ensure de-bottlenecking and to address back-pressure issues of the production networks while flowing wells in their optimal operational envelope to avoid the rapid depletion of the reservoir and the increasing water production. Due to the vast number of wells, the intricate network, the limitations in the gathering system and the dynamic operations, maintaining and sustaining the reservoir performance requires continuous surveillance, rapid and robust understanding of the different variables at reservoir, well and network level to make timely decisions and maximize reservoir recovery. To support this, a standardized and integrated system based on data analytics and numerical models, has been developed in the context of Realize the Limit (RTL) of the Greater Burgan reservoir as a tool for the quick identification of potential oil gain opportunities, the evaluation of bottlenecks across all the production system, and track and measure the impact of different proposed operational and development scenarios to unlock potential production and support optimum production forecasting. The system has been developed in a digital framework, with inputs from a multi-disciplinary team to define the datasets, calculated parameters and visualization requirements related to reservoir properties, well completion status, operational parameters, and key indicators from subsurface and surface hydraulic models. These has been integrated to identify key performance issues involving several aspects of the reservoir's development and operational plans. Also, the process provides a high-level overview and a platform for all asset groups including management, field development and operations, where observing the same set of results can initiate collective decisions that improve wells, network and facility management and enhancement recommendations. 2 SPE-211118-MS
Dashti, Qasem (Kuwait Oil Company) | Abdulrazzaq, Hanan (Kuwait Oil Company) | Al-Shammari, Nouf (Kuwait Oil Company) | Al-Habashi, Haytham (Kuwait Oil Company) | Al-Rumaidhi, Meshal (Kuwait Oil Company) | Talabi, Oluwole A. (Schlumberger) | Franco, Francy Milena (Schlumberger) | Muhammad, Yaser (Schlumberger) | Zhang, Michael Qiong (Schlumberger) | Prakash, Roshan (Schlumberger) | Goula Masllorens, Nicolau (Schlumberger) | Ali, Samad (Schlumberger)
Abstract This paper discusses the development of full pore-to-process integrated asset models (IAM) for the Greater Burgan (GB) oilfield in Kuwait, the largest clastic oil field in the world. The IAM links the reservoir model with the multiple wells, pipelines, network models and process facilities models for improved forecasting and operational excellence in the South and East Kuwait asset of Kuwait Oil Company. The main objective behind the development of this integrated asset model is to enable enhanced asset management and to improve decision making, accounting for the complex interactions and synergies between reservoirs, production networks and process facilities in the hydrocarbon flow path all the way from the reservoir to the export points. The IAMs were developed using calibrated models built using next-generation simulators that enabled the running of forecast scenarios from the pore to process. The reservoir model was developed using a high-resolution reservoir simulator that enabled the simulation of this giant oilfield with more than 2000 wells in a few hours. The reservoir model was then coupled to the full-physics well-and-network models for 3 gathering centres of key interest which had also been previously calibrated to match wells and manifold rates and pressures. Finally, the network model was connected to the high-performance process facility model at the manifold headers. The reservoir-network coupling was done at the well level, each well coupled at the bottomhole with an updated IPR passed to the network and a resulting outflow constraint passed back to the reservoir every timestep to capture any effects of pressure regime established in the network. The network-process facility connection was established by using a feedforward push of the calculated mass flow rate, pressure, GOR, water cut, and temperature at the manifold, as updated boundary conditions to estimate the quantity and quality of fluids produced from the facility. The results of the integrated models showed moderate impact of the network on the performance of the reservoir over a 5-year forecast. Integration of the vast number of wells and network models with the crude processing facilities in a single IAM platform enables the evaluation of oil production improvement opportunities in terms of their long-term dynamic impact on the reservoir management. The IAM models will help to identify the bottlenecks in the system, optimize the production and achieve the aggressive oil target of the GB asset. This is the first set of fully first operational IAMs for Greater Burgan that includes all three key components – reservoir, network, and process facilities. The IAM gives access to control and define constraints in all the component models, making it an effective tool for further analysing development and optimization strategies for improved asset management of the largest clastic oilfield in the world.