Production Optimization Through Utilization of Smart Wells in Intelligent Fields

Abstract

With improvements in technology and increasing amount of opportunities in more challenging assets, the use of smart well technologies to improve recovery has caught significant attention in the oil and gas industry in the last decade. Several workflows have been developed and proposed in order to automate the whole process that integrates several subprocesses focusing on specific parts of the surface or subsurface phenomena. Reservoir sweep is a crucial part of recovery efficiency, especially where significant investment is done by means of installing smart wells that feature inflow control valves (ICVs), which are remotely controllable. However, as it is a relatively newer concept, effective use of this technology has been a challenge. In this study, the objective is to present the efficient use of ICVs in intelligent fields to maximize sweep, and thus, recovery tied to the objective function. A standard realistic SPE reservoir simulation model of a waterflood process has been used where the smart well ICVs are controlled with conditional statements, called procedures, in a fully-commercial full-physics numerical reservoir simulator. Key performance indicators, including but not limited to water cut and oil rate, are used to adjust the degree of opening of ICVs on the producer side to balance injection on the injector side. This turns out to be a complex phenomenon of higher degree of nonlinearity in a multi-well system in a large field where several wells interact with each other. Objective function seeks to maximize the net present value (NPV) of cumulative oil recovery. Smart well technologies have been challenged with the associated cost component, thus, it is important to present the benefits of this technology with applications on more diverse cases showcasing different workflows. It has been observed that robust reservoir management in an intelligent field can significantly improve the sweep and recovery by utilization of smart wells with ICVs. The results are presented in a comparative way against the base case to illustrate the incremental value of use of ICVs, along with key performance indicators. Most importantly, it has been shown that the use of smart wells without a robust reservoir management strategy does not always lead to successful results. In reservoir management, it is not only important to catch the well level details but also to see the big picture at field level for improving reservoir performance beyond individual well performances, taking into account the interference between wells. Although smart wells with ICVs have been deployed on wells all over the field, unfortunately, some similar studies mainly focus on individual or near-wellbore performance rather than the whole asset. In this study, a field-wide approach is followed that integrates data from key performance indicators in an integrated workflow, which outlines efficient integration of variables available to optimize recovery.

Effect of Well Orientation on Oil Recovery from Waterflooding in Shallow Green Reservoirs: A Case Study from Central Africa

“…Despite the volume of published literature [26][27][28][29][30][31][32][33][34][35][36][37][38] on the recovery efficiencies of different well types, to the best of our knowledge, there has been no research on the performance of highly deviated wells in the highly heterogeneous shallow reservoirs of the Central African Rift Valley region, which are significantly different from most moderate-depth to deep reservoirs in other parts of world. This is because of (i) the complications associated with drilling along a precise path within an inclined path, (ii) difficulties encountered in preventing sand influx, and (iii) challenges encountered during screen installation for sand production control, which render directional drilling more difficult and time-consuming for complex reservoirs.…”

Efficient workflow for optimizing intelligent well completion using production parameters in real-time

“…This difficulty in representing completion equipment leads the reservoir engineer in the search for adaptations when modelling the well. Great part of the research in reservoir simulation is made by placing one ICV on each layer, generating a higher number of valves per well, as showed in the work of Almeida et al (2007), Ranjith et al (2017) and Sampaio et al (2015a). While this approach can be effective for individual layer flow analysis, not always this simulation scenario can be replicated on the field.…”

Aplicação De Técnicas De Machine Learning E Data Driven Em Poços Inteligentes De Petróleo

“…Existem várias vantagens na implementação dos PCIs e a principal delas é a otimização da produção de petróleo e gás, que pode ser feita através da otimização da produção de cada poço, aumentando desta forma a produção do reservatório, ou através da redução dos custos operacionais no campo de petróleo, por exemplo, a redução dos custos com intervenções. Além disso, Ranjith et al (2017) cita outras principais vantagens do PCI, como:…”