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ABSTRACT The industry is facing significant challenges due to the recent downturn in oil prices, particularly for the development of tight reservoirs. It is more critical than ever to 1) identify the sweet spots with less uncertainty and 2) optimize the completion-design parameters. The overall objective of this study is to quantify and compare the effects of reservoir quality and completion intensity on well productivity. We developed a supervised fuzzy clustering (SFC) algorithm to rank reservoir quality and completion intensity, and analyze their relative impacts on wells' productivity. We collected reservoir properties and completion-design parameters of 1,784 horizontal oil and gas wells completed in the Western Canadian Sedimentary Basin. Then, we used SFC to classify 1) reservoir quality represented by porosity, hydrocarbon saturation, net pay thickness and initial reservoir pressure; and 2) completion-design intensity represented by proppant concentration, number of stages and injected water volume per stage. Finally, we investigated the relative impacts of reservoir quality and completion intensity on wells' productivity in terms of first year cumulative barrel of oil equivalent (BOE). The results show that in low-quality reservoirs, wells' productivity follows reservoir quality. However, in high-quality reservoirs, the role of completion-design becomes significant, and the productivity can be deterred by inefficient completion design. The results suggest that in low-quality reservoirs, the productivity can be enhanced with less intense completion design, while in high-quality reservoirs, a more intense completion significantly enhances the productivity. Keywords Reservoir quality; completion intensity; supervised fuzzy clustering, approximate reasoning,tight reservoirs development
Abstract 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.
Islamov, Rafael (Petronas Carigali Sdn Bhd) | Motaei, Eghbal (Petronas Carigali Sdn Bhd) | Madon, Bahrom (Petronas Carigali Sdn Bhd) | Abu Bakar, Khairul Azhar (Petronas Carigali Sdn Bhd) | Hamdan, Victor (Petronas Carigali Sdn Bhd) | W M Zani, Luqman (Petronas Carigali Sdn Bhd)
Abstract Dynamic Well Operating Envelop (WOE) allows to ensure that well is maintained and operated within design limits and operated in the safe, stable and profitable way. WOE covers the Well Integrity, Reservoir constraints and Facility limitations and visualizes them on well performance chart (Hamzat et al., 2013). Design and operating limits (such as upper and lower completion/facilities design pressures, sand failure, erosion limitations, reservoir management related limitations etc) are identified and translated into two-dimensional WOE (pressure vs. flowrate) to ensure maximum range of operating conditions that represents safe and reliable operation are covered. VLP/IPR performance curves were incorporated based on latest Validated Well Model. Optimum well operating window represents the maximum range of operating conditions within the Reservoir constraints assessed. By introducing actual Well Performance data the optimisation opportunities such as production/injection enhancement identified. During generating the Well Operating Envelops tremendous work being done to rectify challenges such as: most static data (i.e. design and reservoir limitations) are not digitized, unreliable real-time/dynamic data flow (i.e. FTHP, Oil/Gas rates etc), disintegrated and unreliable well Models and no solid workflows for Flow assurance. As a pre-requisite the workflows being developed to make data tidy i.e.ready and right, and Well Model inputs being integrated to build updated Well Models. Successful WOE prototype is generated for natural and artificially lifted Oil and Gas wells. Optimisation opportunities being identified (i.e. flowline pressure reduction, reservoir stimulation and bean-up) Proactive maintenance is made possible through dynamic WOE as a real time exceptional based surveillance (EBS) tool which is allowing Asset engineers to conduct the well performance monitoring, and maintain it within safe, stable and profitable window. Additionally, it allows to track all Production Enhancement jobs and seamless forecasting for new opportunities.
This article summarizes the fundamental gas-flow equations, both theoretical and empirical, used to analyze deliverability tests in terms of pseudopressure. The four most common types of gas-well deliverability tests are discussed in separate articles: flow-after-flow, single-point, isochronal, and modified isochronal tests. Deliverability testing refers to the testing of a gas well to measure its production capabilities under specific conditions of reservoir and bottomhole flowing pressures (BHFPs). A common productivity indicator obtained from these tests is the absolute open flow (AOF) potential. The AOF is the maximum rate at which a well could flow against a theoretical atmospheric backpressure at the sandface.