A comprehensive review on characterization and modeling of thick capillary transition zones in carbonate reservoirs

A method for detecting pressure disturbances in a formation accessible by a borehole while performing an operation includes positioning a tool within the borehole, positioning a first probe of the tool at a first location, positioning a second probe of the tool at a second location remote from the first location to obtain a pressure reading, performing an operation with the first probe, detecting the presence of a first phase fluid within the tool, detecting a pressure disturbance within the formation with the second probe, and identifying a second phase fluid based on the detection of the pressure disturbance. Other methods and systems for detecting pressure disturbances in the formation are further shown and described.

System and method for detecting pressure disturbances in a formation while performing an operation

Acquisition of oil samples from the reservoir prior to oil production is essential in order to design production strategies and production facilities In addition, reservoir compartmentalization and hydrocarbon compositional grading magnify the necessity to map fluid properties vertically and laterally in the reservoir prior to production Downhole fluid analysis (DFA), performed in situ in the oil well, helps optimize this fluid mapping process However, for heavy oil reservoirs drilled with water based muds, fluid mapping has been largely precluded due to the formation of stable water‐in‐oil (w/o) emulsions, which add significant complexity to sample acquisition and which can preclude standard DFA measurements Here, fluorescence measurements are shown to be dependent on oil type but independent of the state of emulsion even at very high water fractions Thus, downhole fluorescence measurements can be used to perform hydrocarbon fluid mapping in the reservoir The sensitivities of fluorescence and optic

Fluorescence Methods for Downhole Fluid Analysis of Heavy Oil Emulsions


 Drilling boreholes through hydrogen sulfide (H2S) bearing formations such as Umm Er Radhuma and Tayarat Formations poses a critical challenge for the oil and gas industry in southern Iraq. In this era of increased concern for personal safety and environmental factors, the industry needs additional tools and methods for handling this deadly and corrosive gas. This paper describes how actual formation field data is entered into unsupervised learning software to train a H2S monitoring simulator to improve the drilling operators’ ability to detect H2S, analyse and then adjust mud pH to neutralize free H2S present in drilling fluids.
 The H2S concentration measurements for five drilled wells in the Umm Er Radhuma and Tayarat fields in the South of Iraq used in a previous study are extended to include data from an additional 12 wells with 22 H2S events. This paper describes a real-time H2S simulator which consists of a computer interface kit implementing MATLAB code. The kit is provided with three LEDs (green, yellow, and red) representing different alert levels. The system monitors changes in H2S levels and triggers alarms depending on the levels. The setup of the alert levels for the simulator are adopted from both Health Safety Environment (HSE) policy and unsupervised vector quantization via Fuzzy Adaptive Resonance Theory (ART) neural network category ranges.
 Results show that using Fuzzy ART to train the H2S simulator alert levels based on formation data leads to a smarter and more sophisticated alert system compared to the typical HSE set of alerts. Specifically, the Fuzzy ART derived alerts enable earlier detection of H2S events, faster response to changing H2S levels, and it computes workers’ exposure over time to H2S, to prevent excessive accumulation of H2S in the respiratory system.
 The system demonstrates a smarter and more robust method for reducing risks to drilling personnel, rig equipment, and the environment while drilling in areas with H2S hazards.

Learning from Experience: Real-Time H2S Monitoring System Using Fuzzy ART Unsupervised Learning

In drilling and well completion operations, drilling fluid is a crucial element as it is employed for the purposes of several functions. The main functions of drilling fluid are to control formation pressure, maintain the wellbore stability, transport the cuttings up to surface to clean the borehole bottom as well as to lubricate and cool the drill bit. Moreover, it is used to minimize the drilling damage to reservoir and suspend cuttings when the pumping is stop, hence it will not falling back down the borehole. The purpose of this study is to formulate new drilling mud formulation modified with nanosilica. Six samples of water based mud (WBM) were prepared using three types of polymers, (Xanthan Gum, Hydro Zan Plus and Hydro Star HT), starch and nanosilica. Basic rheological tests such as density, viscosity and pH were carried out. The density test was carried out using mud balance meanwhile the pH test was using pH meter. The plastic viscosity, yield point and gel strength tests were carried out using viscometer. Besides that, physical observation was also performed for as the stability test. The results concluded that water based mud incorporated with polymer Hydro Zan Plus and nanosilica can be a potential candidate to be commercialized as a smart nanodrilling fluid.

Amal Al Hosani

Papers

Downhole pH Measurement for WBM Contamination Monitoring and Transition Zone Characterization

Applications of Real-Time Downhole pH Measurements