Data Driven Field & Well End-to-End Environmental Impact Evaluation Methodology for AICD
Authors: Asthana P. , Patil P. , Jacob S. , Arackakudiyil Z. S. 
Published: April 2024

Autonomous Inflow Control Devices (AICDs) are being increasingly used for downhole produced water reduction and to increase oil recovery. AICD technology is typically fluid dependent and reacts to the change in properties of fluid flowing through it. Most AICDs react to the viscosity change of the flowing fluids and create an additional pressure drop to restrict the unwanted fluids (Kalyani et al., 2022). Downhole reduction of water produced directly reduces carbon footprint in terms of water handling, reduction of water injection and extending the well life This paper describes a methodology to estimate life of well carbon footprint and carbon intensity for oil wells installed with different inflow control well devices and will highlight benefits offered by autonomous device during installation. The data input and design modelling workflow will be described for a Density based AICD that provides active water management in multiphase production operating without electric or surface control, and reacting to the density change of the flowing mixture. The presented analysis is scalable to a Field-Level to help in assessing the overall environmental impact of Density based AICDs.

A methodology and digital tool kit have been developed that allows lifecycle estimation of overall carbon footprint and carbon intensity map and trends for typical ICD completed Wells. The analysis considers all impacts of ICD installation, operation, workover and provides End-to-End estimation of overall carbon footprint. The tool kit consists of a performance calculator that selects the ideal ICD characteristics and feeds a customized reservoir interface steady state numerical simulator along with data on candidate well such as Production Logging, Permeability and Pressure-Volume-Temperature information. The output predicts zonal flow profile for optimized oil production and water choke back rates for the well, and produces time based cumulative and year-on-year carbon equivalent footprint and carbon intensity maps through predictive environmental impact calculations based on industry knowledge and published guidelines. To demonstrate the methodology, a hypothetical synthetic field model was created with assumed characteristics to allow for comparison study between different inflow control technology combinations in same conditions. The methodology is applicable to any AICD or passive ICD technology.

AICDs balance inflow across the production zone during initial production and automatically restricts the rate from zones producing water later in-well life. This analysis is conducted over the full well lifecycle considering early, mid and late life stages. The resultant time-series data is converted to Well-Level carbon equivalent footprint map over time by evaluating against total oil production from the Well. Performance of passive ICD versus autonomous Density-AICD is evaluated for environmental gains in the simulated wells. This methodology can be extended to multiple wells in a cluster and provide a Field-Level carbon equivalent footprint visual heat map. Furthermore, Carbon intensity trends are derived for target Wells and Field. The results can help optimize the selection of each Density-AICD and fine tune zonal placements and choke sizes. This enables yearly carbon savings to be in line with regional and global upstream goals.

This analysis assesses the environmental impact of Density-AICDs for Oil operators to review against regional and global sustainable development Goals (SDGs). This tool kit will help operations teams and executives from the Operator evaluate high impact oilfields with water breakthrough and reveal opportunities to accelerate optimization of planned and existing wells to reduce water production.