Polymer flooding is a mature EOR technique successfully applied commercially in sandstone reservoirs and at the pilot stage in carbonate reservoirs. However, all previous pilots in carbonates reservoirs were implemented in relatively low temperature and low salinity conditions. No field application of polymer in carbonate was implemented in the last 25 years. In recent EOR screening studies for carbonate reservoirs in Abu Dhabi, polymer based EOR techniques were identified to target by-passed oil in heterogeneous/layered reservoirs The main challenges for polymer based EOR processes in ADNOC reservoirs is to find a stable polymer under the extreme conditions of high temperature/high salinity/high content of divalent cations which can be injected in carbonate reservoirs. An extensive laboratory program initiated 10 years ago led to the development of a polymer rich in Sodium Acrylamido tertiobutyl Sulfonate (ATBS). Thermal stability, bulk and in-situ rheology, adsorption and injectivity performed and the polymer was found suitable for the harsh conditions of ADNOC reservoirs. A de-risking strategy was designed in which a polymer injectivity test (PIT) followed by a multi well pilot are performed before the full field implementation of polymer based EOR for a number of ADNOC reservoirs. This paper describes in details the main steps of the successful PIT recently carried out including: the selection of the candidate well and the injection skid, the test design, its execution, the polymer solution quality management and the operational challenges faced during the pilot. The polymer Injectivity Test was conducted for 4 months and concluded by February 2020. A total of 150,000 barrels of viscous solution was successfully injected into the reservoir. This paper also details the real time surveillance and injection monitoring plans implemented during the test period for real time assessment of the skid delivery and the well response. This Injectivity test achieved the designed Key Performance Indicators related to polymer solution quality, viscosity, concentration, injection rate and skid running time. The dedicated surveillance and injection monitoring plan designed and implemented during this pilot, enables to confirm the good performance of the polymer during PIT period. Furthermore, PIT results showed good performance of Polymer in terms of viscosity, Injectivity at target rate and concentration. This paper also addresses the impact of water quality on polymer viscosity and skid operation. This paper presents field results for a new polymer developed for carbonate reservoirs at HT/HS. This successful Polymer Injectivity test qualified the new polymer for field application at harsh carbonate reservoir conditions. Results from this world first Injectivity test opens a new area of possibilities to improve recovery in giant heterogeneous carbonate reservoirs in ADNOC and in the Middle East.
Productivity enhancement of tight carbonate reservoirs (permeability 1-3 md) is critical to deliver the mandated production and to achieve the overall recovery. However, productivity improvement with conventional acid stimulation is very limited and short-lived. Tight reservoirs development with down spacing and higher number of infill wells can increase the oil recovery. Nevertheless, poor vertical communication (Kv/Kh < 0.5) within the layered reservoir is still a challenge for productivity enhancement and needs to be improved. First time successful installation of fishbone stimulation technology at ADNOC Onshore targeted establishing vertical communication between layers, in addition to maximizing the reservoir contact. Furthermore this advanced stimulation technology connects the natural fractures within the reservoir, bypasses near well bore damage and allows the thin sub layers to produce. This technology requires running standard lower completion tubing with Fishbone subs preloaded with 40ft needles, and stimulation with rig on site. This paper presents the case study of the fishbone stimulation technology implemented at one of the tight-layered carbonate reservoir. A new development well from ADNOC Onshore South East field was selected for implementation of this technology. The well completion consisting of 4 ½ liner with 40 fishbone subs was installed, each sub containing four needles at 90 degrees phasing capable of penetrating the reservoir up to 40 ft. While rig on site, acid job was conducted for creating jetting effect to penetrate the needles into the formation. Upon completion of jetting operation, fishbone basket run cleaned the unpenetrated needles present in the liner to establish the accessibility up to the total depth. Overall, application of this technology improved the well production rate to 1600 BOPD compared to 400 BOPD of production from nearby wells in the same PAD and reservoir. In addition the productivity of the candidate well improved by 2.5 times with respect to near-by wells in the same PAD. Currently, long-term sustainability testing preparation is in progress. This paper provides the details of candidate selection, completion design, technology limitations, operational challenges, post job testing and lessons learned during pilot implementation. In summary, successful application of this technology is a game changer for tight carbonate productivity enhancement that improves the overall recovery along with optimizing the drilling requirements. Currently, preparation for implementation of 10 pilots in one of the asset at ADNOC Onshore fields is in progress.
This paper details the implementation plan of the first Digital Intelligent Artificial Lift (DIAL) gas lift production optimization technology in a dual string well in the UAE, with a specific focus on completion and installation considerations. Optimizing gas lift systems with existing technology is typically time consuming, costly and risky. Frequent well interventions are required with associated lost and/or deferred production. Traditionally it was not possible to make on-demand in-well adjustments to gas lift injection depth and rate to address these challenges. Compounding this, it was not possible to easily make data-driven decisions about these adjustments to assure continuously maximized and stable production. These challenges are further amplified with dual completion strings: fluctuating casing pressure; unpredictable temperatures due to the proximity of the two strings; and inability to individually control the injection rates to each string. String dedicated to the formation with lower productivity and reservoir pressure tends to "rob" gas from other string. Operating philosophy in such cases end up producing from one string. Production optimization in such cases requires frequent intervention with attendant costs and risks thus presents an opportunity to re- imagine gas lift well design. ADNOC in collaboration with Silverwell developed a Digital Intelligent Artificial Lift (DIAL) system, which consists of multiple port mandrels to be placed at GLV depths. These mandrels are connected to the surface operating system with a single electrical cable. The ports can be selectively opened or closed by sending an electric signal from the surface unit. In addition, pressure and temperature sensors are also placed which help record these parameters in real time. Such a system enables the choice of depth, injection rate, loading and unloading sequence controlled from the surface. Realtime optimization is possible as pressure/temperature data helps draw accurate gradient curves. This system makes gas lift optimization possible in dual gas lift wells. It has been estimated that this technology delivers a production increase approaching 20% for single completion wells, and exceeding 40% for dual-string gas lifted wells. Recognizing this opportunity, a business case and implementation plan were developed to pilot a dual-string digitally controlled gas lift optimization system. This paper will describe, the screening phase, business case preparation, risk assessment and validation process, leading to this 1st worldwide implementation of a fully optimized dual completion gas lifted well. Implementation plan of novel digital gas lift production optimization technology in an onshore dual completion well. The completely original approach increases safety, efficiency, operability and surveillance. The paper reintroduces work presented in paper SPE-196146-MS at the SPE Annual Technical Conference and Exhibition in Calgary, with updates on the candidate well selected and further information given on the completion details and installation preparations.
Water injection is seen as one of the key field development strategies to achieve the mandated production target as it will maintain reservoir pressure as well as improve sweep efficiency and increase field recovery factor. In current practices water supply wells workovers are planned after Electrical Submersible Pumps (ESP) are failed by adopting run to fail approach. This lead to decrease in well availability and increase in down time which impacts water injection cluster capacity in giant matured onshore oil field. The objective of this solution is to early detect the failures for ESP wells using Machine Learning (ML), by demonstrating the feasibility of this approach and verifying that the concept has practical potential, the tool can be used to reduce deferment and increase well availability either by extending time-to-failure or better planning and scheduling the workovers. In this solution, Predictive Analytics model was developed based on Algorithms using field sensor data, and well failure history to predict ESP well failure probability. Due to the limited available ESP real time data, it would be a challenge to have an accurate model. The downhole and temperature data is not available in these ESP wells. Hence, we have adopted unsupervised classification approach combined with statistical calculations such as MTBF based on failure history. The solution provides a probability of ESP failures based on the anomalies (anomaly severity) detected from unsupervised machine learning model (individual cluster based), MTBF & number of starts. The probability is normalized based weight-based approach. Additional criteria can be added and considered in the future to fine tune the model and predictions. The approach has successfully evaluated on 34 water injection clusters in this giant field. The model is able to predict 77% of failures historical failures successfully. The limitations in ESP down-hole data availability and real time quality issues impacted model accuracy. The solution has been successfully deployed in real time mode and able to predict failures 90 to 120 days before failures. This has resulted increase in well availability by 10% and increased water injection system capacity. This machine learning based approach has been extended to all water injection clusters and also capitalized in other fields to increase well availability and grow capacity with the increasing demand for water injection to sustain and grow production volumes
In brownfields, controlling well integrity is critical in maintaining production and ensuring safety of the personnel and infrastructures. Equally important is optimizing and allocating production in wells by closely following wellhead upstream pressures (and temperatures). In the current situation, field crews have to move from well to well. This method is time consuming, exposes personnel to driving hazards and potentially dangerous areas. In addition, human reading of manual pressure gauges can result in large discrepancy in the reported values. Together with the low frequency of manual readings, this method does not allow for pro-active well intervention and can result in higher downtime in case of well tripping. Deploying remote monitoring with classical telemetry in fields with limited telecommunication infrastructure is costly and complex. Low Range Wide Area Network (LoRaWAN), a public wireless network technology developed in 2009, changes the situation. It enables low power compact battery sensors with up to 10 km radio range. This performance is sufficient to connect, in one go, most onshore wells without power nor connectivity. This paper describes a pilot project to evaluate the adequacy of this technology in ADNOC Onshore fields. The objective is to assess performance of LoRaWAN deployed Sensors along four metrics: deployment time, deployment cost, Base station radio coverage and data availability. The pilot uses a plug-in ATEX- certified Wireless Pressure and Temperature (P&T) sensors developed by the vendor SRETT, commercial LoRaWAN Base stations, and proprietary software to provide remote access to the data via cloud data storage and web based application. For this pilot, four Base stations were deployed in two giant oil fields collecting data from four well heads each equipped with two sensors (P&T). This combination allowed testing wireless link quality over eight radio paths, some with terrain obstacles between Sensors and Base stations. The complete system was fully tested and validated at the shop prior to field deployment. Performances during the deployment was evaluated, and Sensor behaviors were monitored over a three-month period. In the current environment, maintaining a high HSE standard on aging infrastructure must be made at a controlled cost. LoRaWAN IoT remote monitoring technology is cost effective and efficient to deploy. Once deployed, it will enable preventative safe detection of wells with potential issues, improved accuracy and understanding of production events and lead to a reduction of potential adverse situations thanks to an optimized intervention strategy.
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