First Open Hole Gravel Pack with AICD in Ultra Deep Water

Abstract

The execution of Open Hole Horizontal Gravel Packs (OHHGP) in ultra-deep waters (greater than 1500m) demands the use of the best technologies available. For the Atlanta Project, a detailed description of the first two wells successfully completed with OHHGP in the field, was provided in previous papers (OTC-25813, SPE-174893, SPE-174896). The third Atlanta well faced a new challenge: the integration of AICD technology (Autonomous Inflow Control Device) in the gravel pack screens. Due to the restriction imposed to the flow through the screens by the AICDs, the resulting pumping pressure and associated ECD (Equivalent Circulating Density) is increased, further reducing the already narrow operational window, which is common for reservoirs in such water depths. The use of AICD technology, which imposes an additional pressure drop to the aqueous fluid flow, further increases the pumping pressure, making conventional gravel packing strategies and procedures unfeasible. To overcome these new challenges, dozens of simulations were performed to address the new challenge and an innovative screen configuration was adopted to facilitate effective gravel pack placement while retaining the necessary fluid flow restriction of the AICD operating as per the simulation design during well production. Additionally, to ensure effective and complete gravel pack efficiency, a new pumping procedure, incorporating the use of three different proppant strategies, was implemented. By introducing these innovations, the third Atlanta well was 100% packed and the expected PI was obtained. Long-term performance, with the aid of tracers installed in the screens, is being evaluated. This paper will detail all the planning and execution phases that enabled completion of the first open hole gravel pack with AWES RP T2 AICDs in ultra-deep waters. Also in this paper, the production evaluation data is analyzed.

Application of mathematical and machine learning models to predict differential pressure of autonomous downhole inflow control devices

“…Fig. 2 illustrates the general structure and size of Equiflow AICDs and the fluid flow-pathways for high (such as heavy oil) and low (like water) viscous fluids (Pedroso et al, 2020).…”

“…Therefore, a practical method must be able to model the performance of the Equiflow AICDs accurately in field conditions based upon a limited set of data. Accurate AICD prediction models are beneficial for reservoir simulation, assisting them to accurately predict the production rate and efficiency of reservoirs on a fieldwide basis (Eltaher, 2017;Pedroso et al, 2020). Conventional autonomous flow control device (AFCD) formula, Halliburton model, Aadnoy model and the mathematical AICD performance model are the most popular mathematical models used to estimate the differential pressure of AICDs (Jovanov, 2016;Eltaher, 2017;Eltaher et al, 2019).…”