TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThis paper describes solutions developed using a dynamic surveillance tool to automate several workflow processes of the reservoir management, production engineering, and R&D Center at Saudi Aramco. The objective is to provide improved efficiency in field management practices, while enhancing collaboration between reservoir and production engineers; ultimately resulting in improved decision-making process.The solutions provided include a combination of smart tools and automated workflows designed to improve reservoir management and surveillance processes. A candidate recognition system was developed to identify and flag problem wells that require immediate remediation. As new production and injection data become available, the system that is linked to the corporate database can automatically display these data for fast and rigorous validation. In addition, a formation damage indicator function is also calculated using field data and mapped to spot production problem areas and identify damaged wells. A daily surveillance tool, which compares the performance of individual wells to the average performance of a group of wells, is also provided to allow the reservoir and production engineers to easily identify underperforming wells, promptly intervene, and recommend best completion practices. Benefits include efficient well management and cost avoidance resulting from early intervention and remediation, while avoiding full-scale problem resolution.Another dynamic surveillance tool was designed and views were developed to provide online access to the hydrocarbon phase behavior and petrophysical data for the R&D scientists and reservoir engineers. The tool allows integration of the hydrocarbon phase-behavior data and comparison of petrophysical data with historical production/injection data and production well logs, resulting in enhanced analysis, production optimization and data validation. Additional benefits of the smart tools and automated workflow processes include considerable timesavings, with pertinent data being automatically updated, validated and used in the analysis, leading to improved efficiency in field management practices.
This paper presents a new automated workflow to diagnose and evaluate water production signatures for a mix of vertical and lateral wells in a carbonate reservoir under water-flooding. The workflow can be used for rate optimization, remedial intervention planning and for reservoir description. The workflow basically includes decline curve analyses for oil and water productions, Chan analysis for water production signature, and evaluation of the combined decline curves and water signatures for estimating well ultimate cumulative oil and reservoir description. This enables reservoir engineers in optimizing the production rate and in planning future remedial actions to prolong well life and maximize recovery. A new event-based Chan analysis is proposed to better assess the change in water production signature after each remedial event such as acid treatment, water shut-off or sidetracking. The study reveals that the benefits of the automated workflow are significant for reservoir engineers who deal with complex reservoirs with numerous wells. The well ultimate cumulative oil is estimated more accurately using the combined decline curves of oil and water production. Using the proposed event-based Chan analysis, it is possible to identify the change in water production signatures of wells of different types before and after well events. The water problem diagnostics are also helpful to understand the highly-conductive features in complex carbonate reservoirs.
Water management has always been a challenge especially in mature fields. Consequently, mechanical, chemical shutoff and other water reduction techniques have been developed and deployed to curb the menace in the hydrocarbon industry. However, poor diagnostic work can be a leading reason for the low success rate for any water control method. This paper introduces a holistic workflow to understand the candidate selection, filter the wells based on priority and determine the water breakthrough mechanism to eventually select the optimal remedial action. In this paper, 7 wells are selected and prioritized to undergo a workflow to diagnose water breakthrough and characterize it. The first analytical tool is Chan correlation, which incorporates the water-oil ratio for determining the water signature. For determining the water entry zone, Production Logging Tool (PLT) will be used as the second investigative tool. Water source identification plays another major role in assessing whether the water is coming from an aquifer, nearby injector or native reservoir fluid, which can be determined by the frequent sample collection and lab analysis for ionic concentration. These three investigative tools will provide a basis to select the proper water management strategy. The results of the diagnosis have revealed several facts regarding the aforementioned parameters. A number of the diagnosed wells have shown a steep increase in oil-water ratio and oil-water ratio derivative, which hints to a possible nearby thief zone according to Chan correlation. Reviewing the produced water ionic concentration suggests low salinity and that the water's chemistry is closer to that of an injected water than reservoir water. Finally, Production Logging Tool showed multiple water entries in the open hole section. According to the diagnosis, Inflow Control Device deployment for those wells are recommended. Couple of multilateral wells completed with Inflow Control Valves (ICV) showed rapid channeling of water, which can be caused by a thief zone or a lateral dominating the flow and contributing high water cuts. These wells were subjected to ICV optimization and it confirmed that a latera was dominating the flow with high water cut and was optimized. The water cut for those two wells dropped by 58%. The workflow enables engineers to understand the water breakthrough mechanism in a timely-matter, which allows them to categorize the wells based on the different water signatures such as water coning, thief zone, and near wellbore breakthrough. The proposed workflow can be adopted and adjusted based on the water management problems associated to any field in order to find the optimal remedial action. This outcome played a role in the planning of placing and drilling new wells in the field.
One goal for oil fields of the future is acquiring continuous and on-demand data as required for field and reservoir management. Synthetic time-lapse production methods are becoming a way of providing this information at and away from wells. Time-lapse production log data acquired over oil fields is used to monitor water sweep in the reservoir. Production logs provide a direct measure of the fluid flowing downhole and detect the unwanted fluid entries. In field applications, this advanced scanning of fluid profiling successfully derisked several infill well locations and identified new workover candidates and drilling opportunities in the fields. Synthetic time-lapse production logging is a useful complement to understanding reservoir heterogeneity and complexity through tailored synthetic and real data integration. A computer-based workflow has been developed to automate the downhole production flow profile. Production performance of the well is assessed, considering the dynamic time-lapse logging data. A synthetic flow profile is constructed to show the change in water production signature, and the well is further examined if it undergoes remedial actions. Reservoir characterization is a continuous process during the life of the oil field. As new data are available, the model is updated and contains more details. The incorporation of all data allows increased accuracy and reduced uncertainty in characterizing the reservoir. The proposed methodology requires the acquisition of dynamic production logging data to establish a solid workflow and validate the model. Uncertainty can be eliminated with the acquisition of additional production logs. Recommendations for improvement of the current well condition can be made to reduce the well water cut and improve oil production from the well. Consequently, well classification and candidate selection for workover can be achieved. The results of this work demonstrate the strength of applying multidisciplinary team efforts to develop automated workflows that are relevant to reservoir and production engineers who deal with complex reservoirs with numerous wells.
Well integrity in the oilfield is one of the challenges that petroleum engineers face, as they seek to monitor well corrosion in the field to optimize well performance. Most of these fields can be categorized as brownfields, with some of the wells considered aged and have expected integrity issues. To achieve sustainable production targets with cost-effective and safe operations from these fields requires a close monitoring of the integrity of all elements involved in the production chain. Addressing these challenges requires the engineers to coordinate and analyze several data elements, including casedhole, openhole, reservoir, well, and production data from multiple sources. Another challenge is to create and automate a corrosion workflow that saves the engineers’ time and improves efficiency. In this paper, we introduce an innovative workflow that uses the historical corrosion data while integrating the multiple production and reservoir variables. The innovative approach uses machine learning (ML) algorithms to provide a powerful tool for workover (W/O) candidate selection and for optimizing the corrosion evaluation frequency, which are required in different areas of the fields. Different ML methods (random forest classification and neural net) were applied on training data. Different models were created, and the best model will be used. This offered key insights on the rate of corrosion and corrosion patterns. Further, the developed workflow was designed to be self-sustaining and acting as a surveillance tool for monitoring the integrity of the wells. The first step of the workflow was to start with organizing and auditing the available corrosion data, followed by a review and analysis of existing openhole, casedhole, production, and reservoir engineering data. This approach led us to understand the extent and severity of corrosion in terms of the corrosion rate and the corrosion index. The corrosion logs were digitally interpreted depth-wise in order to explore the maximum metal loss for each interval. New animated conformance corrosion maps were created. The successful diagnosis through data analytics in a modern integrated software platform will assist in corrosion monitoring and decision-making. The multiple corrosion maps can be animated to visualize the current corrosion profile and predict the corrosion over time, in addition to ranking the wells for W/O candidate selection.
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