A Systematic Approach to Intelligent Well Performance Modelling Using IPM Suite

Edih, Michael; Nnanna, Erasmus; Nwankwo, Cosmas

doi:10.2118/184328-ms

Cited by 3 publications

References 5 publications

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Using combinatorics to compute fluid routing alternatives in a hydrocarbon production network

Stanko

Venstad

2016

J Petrol Explor Prod Technol

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Typically, hydrocarbon production networks have several fluid routing alternatives that are applied by opening and closing on-off valves. This usually sends the wells' fluids through a specific pipeline, pump or compressor, or to a particular separator, among other requirements. This paper presents a general methodology to compute all fluid routing configurations of a production network using a graph representation of it. The particular implementation case discussed in this paper involves interacting with a preexisting steady-state computational model of the production network. The method starts by extracting from the model the name list of elements in the network and their type. Equipment (wells, separators, junctions, pumps, compressors, valves, etc.) is tagged as nodes and pipes (flowlines, connectors) are tagged as edges. The nodes are further classified by type: sources (wells), internals (e.g., junctions) or sinks (separators). The start and end element of each edge is recorded. This process yields a network connectivity list. A depth-first search is executed from each source to each sink. The search keeps track of the edges that must be active in each path and honors (if any) pre-specified edge directions. All paths for one source node in one component are combined to form all feasible edge combinations for that source node, and these combinations are again combined for all the source nodes in each component. This is repeated for all graph components. The unique combinations are stored and reported at the end.The method has been tried in a production network with seven wells representing a typical subsea production system in the North Sea where the wells have the option to produce (through two flowlines) to two separators on the platform. The production network model was available in a commercial software; thus, there was no access to the code or the underlying equations. The model was controlled from an external computational routine using automation. The graph was extracted from the model, all operating configurations of the network were computed (2187), and then each one was applied (by enabling or disabling flowlines) and evaluated in the commercial software. This allowed to identify routing configurations that provided maximum total oil production or maximum total gas production. There were only 306 configurations that yielded a total oil production close (within 10%) to the maximum recorded oil production. The input data of the production system model are given in the appendix for verification and benchmarking by a third party. Details about the implementation are provided.

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Using combinatorics to compute fluid routing alternatives in a hydrocarbon production network

Stanko

Venstad

2016

J Petrol Explor Prod Technol

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Reactive vs Proactive Intelligent Well Injection Evaluation for EOR in a Stratified GOM Deepwater Wilcox Reservoir using Integrated Simulation-Surface Network Modeling

Sie

Oghena

Seth

et al. 2019

SPE Annual Technical Conference and Exhibition

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Improved field management for monitoring, estimating zone productivity/injectivity, and controlling wells with intelligent completions can broaden application of advanced well designs. We have developed a coupled Simulation-Surface Network modeling workflow to evaluate the potential benefit of intelligent injection profile control with a focus on reactive vs proactive control for Gulf of Mexico (GOM) Deepwater Enhanced Oil Recovery (EOR) schemes. The developed injection control solution can be consistently applied to gas, water injection, and gas followed by water injection, to evaluate relative impacts of intelligent injectors on each option. We did this by defining rules for both proactive and reactive injection ICV controls for a GOM Deepwater Wilcox multilayered reservoir. Proactive controls, based on reservoir zone characteristics, pore volume injected, and recoverable pore volume, are dependent on a static reservoir model realization. Proactive control results demonstrate a diminishing return as we begin to observe fluid breakthroughs that results in part from the inevitable uncertainty of the original static assessment so there should be a benefit in reassessing optimal pore volume injection based on reservoir model updates. Reactive control strategies based on measured production response is a challenge in terms of linking injection control events to production responses that are time-lagged and incomplete for understanding gas and water breakthrough. The integrated model captures the effects of topside facilities, risers, flowlines, pressure/temperature at manifolds and topside, seafloor booster pump performance, wellheads, and wellbore to reservoir interactions and ICV controls to provide a realistic evaluation of achievable development alternatives outcomes.

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Forecasting Intelligent-Well Completion Production through a Coupled Reservoir Model with Custom Functionality

Avila

2020

Day 3 Wed, May 06, 2020

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Intelligent well completion (IWC) or smart wells have been used in the industry for more than two decades. Numerous benefits from this technology have been reported in the literature. These benefits are associated with improved field development economics and better reservoir management. The field economics are mainly impacted by reduction of the well count and the increase of the estimated-ultimate recovery (EUR) and net-present value (NPV) by commingling different reservoirs. The reservoir management benefits are less tangible and more difficult to quantify. They have to rely on certain assumptions that may be controversial among the team members and management. Therefore, the IWC business case is usually supported only by the improved economics. A key input on the economics is the production forecast. The industry lacks a standard workflow to predict the production from an IWC. Reservoir engineers have to be creative using their available tools to model the performance of this complex well operation. The methods regularly used are simple inflow-performance relationships (IPR), nodal analysis, well modeling with pipeline-network simulators, and 3D reservoir simulators with multi-segment well capabilities. The latter is the most appropriate to capture the value for new developments, but still extensive customization is required to optimize the production forecast while honoring all the constraints in the field operational philosophy; the others are more suitable for routine production optimization. In our case, deepwater Gulf of Mexico (GOM), the operating philosophy is (1) use primary well-head choke to control well production, (2) limit the pressure drop across the inflow-control valves (ICV), and (3) limit the producing zone drawdown. In addition, the productivity index (PI) degradation mechanism has to be included in the forecast. None of the commercial reservoir simulators have these requirements built-in. This paper presents an approach that integrates detailed wellbore modeling, reservoir simulation, and the field operating philosophy. The workflow is based on a pipeline network simulator (GAP™) coupled to 3D reservoir simulation models in INTERSECT™. The IWC optimization under the operational philosophy is executed through a Python script—INTERSECT custom functionality—that manages the GAP network information obtained from OpenServer™ and the reservoir simulation data to select the ICV settings that optimize the well production while honoring all constraints. This workflow provides an accurate production forecast assuring all critical operating constraints are incorporated.

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A Systematic Approach to Intelligent Well Performance Modelling Using IPM Suite

Cited by 3 publications

References 5 publications

Using combinatorics to compute fluid routing alternatives in a hydrocarbon production network

Using combinatorics to compute fluid routing alternatives in a hydrocarbon production network

Reactive vs Proactive Intelligent Well Injection Evaluation for EOR in a Stratified GOM Deepwater Wilcox Reservoir using Integrated Simulation-Surface Network Modeling

Forecasting Intelligent-Well Completion Production through a Coupled Reservoir Model with Custom Functionality

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