Iranian Journal of Oil and Gas Science and Technology

Abstract Gas lift is a principal technique used for artificial lifting and Enhanced Oil Recovery (EOR) method, facilitates oil flow in mature fields those in the latter half of their productive life by injecting high-pressure gas into the wellbore to reduce column density. However, the thermodynamics of gas injection in wells requiring high differential pressures introduce severe flow assurance challenges. The intense Joule-Thomson cooling effect across injection chokes is the primary driver of gas hydrate formation. In the studied field (Aghajari), the current hardware-based mitigation strategy employs Thermal Chokes, which utilize the enthalpy of live crude oil to heat the injection gas. Despite this, operational evidence indicates that during cold seasons and for wells with high pressure drops, this system proves inefficient, leading to freezing in injection lines and flow interruption. In the absence of inhibitor injection systems, operators are compelled to resort to reactive measures such as flaring injection gas to induce pressure shocks and clear blockages. This vicious cycle not only results in capital loss but also leads to production deferment and excessive workload for human resources. This research aims to propose a proactive process-based solution by synergizing data mining and computational intelligence. Through the analysis of 5,960 operational records from 101 wells (extracted from the WIMS system), an Artificial Neural Network (ANN) model was developed to serve as a virtual sensor, predicting gas thermodynamic behavior and post-choke temperature with 98.5% accuracy. The core novelty of this study lies in the simulation and validation of a dual-stage pressure reduction strategy. Results demonstrate that splitting the pressure drop profile reduces cooling intensity by up to 60%, maintaining the fluid outside the hydrate stability zone throughout the expansion path. This approach enhances safety and production stability while eliminating the need for costly physical interventions.

Abstract Drilling fluids require effective shale inhibition while maintaining rheology and fluid-loss control, especially in polymer-based water-based drilling fluids (WBMs). Alum (KAl(SO₄)₂·12H₂O) is an eco-oriented chemical widely used in water treatment, yet its concentration-dependent performance in active WBMs remains poorly quantified. This study systematically evaluated alum (0–10 lb/bbl) using a component-based approach and a field-representative polymer WBM, focusing on pH evolution, rheology, filtration, and volumetric swelling in both bentonite and grey marl (calcareous shale).

Alum caused strong acidification in all systems, driving the initial pH of the field WBM down from 10.1 to 4.6 at a concentration of 6 lb/bbl. In the field-representative mud, alum progressively improved rheological profiles up to a critical concentration of 6 lb/bbl, where plastic viscosity and yield point reached their peak performance zone due to temporary polymer–Al³⁺ interactions. Beyond these 6 lb/bbl thresholds, severe pH reduction below 4.0 promoted acid-catalyzed polymer degradation, causing rheological collapse and sharply increasing API fluid loss from 1.1 ml to 2.4 ml.

Swelling tests showed a monotonic reduction in sodium bentonite swelling, stabilizing at 22.77% expansion at 6 lb/bbl. Conversely, grey marl exhibited a non-linear response; swelling was initially minimized to 17.56% at 7 lb/bbl but experienced a distinct swelling reversal at higher dosages, rising back to 23.78% at 10 lb/bbl due to acid–carbonate reactions that undermined mechanical matrix integrity.

These quantitative results define a narrow operational window for alum in polymer-rich WBMs (≤ 6 lb/bbl) and demonstrate that carbonate-bearing shales suffer severe chemo-mechanical instability at elevated dosages, bounding alum's practical applicability in active drilling environments.