With the decline in oil discoveries during the last decades it is believed that EOR technologies will play a key role to meet the energy demand in years to come. This paper presents a comprehensive review of EOR status and opportunities to increase final recovery factors in reservoirs ranging from extra heavy oil to gas condensate. Specifically, the paper discusses EOR status and opportunities organized by reservoir lithology (sandstone and carbonates formations and turbiditic reservoirs to a lesser extent) and offshore and onshore fields. Risk and rewards of EOR methods including growing trends in recent years such as CO2 injection, high pressure air injection (HPAI) and chemical flooding are addressed including a brief overview of CO2-EOR project economics.

Enhanced Oil Recovery: An Update Review

EOR: Current Status and Opportunities

Precipitation of asphaltene is considered as an undesired process during oil production via natural depletion and gas injection as it blocks the pore space and reduces the oil flow rate. In addition, it lessens the efficiency of the gas injection into oil reservoirs. This paper presents static and dynamic experiments conducted to investigate the effects of temperature, pressure, pressure drop, dilution ratio, and mixture compositions on asphaltene precipitation and deposition. Important technical aspects of asphaltene precipitation such as equation of state, analysis tools, and predictive methods are also discussed. Different methodologies to analyze asphaltene precipitation are reviewed, as well. Artificial neural networks (ANNs) joined with imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) are employed to approximate asphaltene precipitation and deposition with and without CO2 injection. The connectionist model is built based on experimental data covering wide ranges of process and thermodynamic conditions. A good match was obtained between the real data and the model predictions. Temperature and pressure drop have the highest influence on asphaltene deposition during dynamic tests. ICA-ANN attains more reliable outputs compared with PSO-ANN, the conventional ANN, and scaling models. In addition, high pressure microscopy (HPM) technique leads to more accurate results compared with quantitative methods when studying asphaltene precipitation.

Asphaltene precipitation and deposition in oil reservoirs –technical aspects, experimental and hybrid neural network predictive tools

Asphaltene is a component of crude oil that has been reported to cause severe problems during production and transportation of the oil from the reservoir. It is a solid component of the oil that has different structures and molecular makeup which makes it one of the most complex components of the oil. This research provides a detailed review of asphaltene properties, characteristics, and previous studies to construct a guideline to asphaltene and its impact on oil recovery. The research begins with an explanation of the main components of crude oil and their relation to asphaltene. The method by which asphaltene is quantified in the crude oil is then explained. Due to its different structures, asphaltene has been modeled using different models all of which are then discussed. All chemical analysis methods that have been used to characterize and study asphaltene are then mentioned and the most commonly used method is shown. Asphaltene will pass through several phases in the reservoir beginning from its stability phase up to its deposition in the pores, wellbore, and facilities. All these phases are explained, and the reason they may occur is mentioned. Following this, the methods by which asphaltene can damage oil recovery are presented. Asphaltene rheology and flow mechanism in the reservoir are then explained in detail including asphaltene onset pressure determination and significance and the use of micro- and nanofluidics to model asphaltene. Finally, the mathematical models, previous laboratory, and oilfield studies conducted to evaluate asphaltene are discussed. This research will help increase the understanding of asphaltene and provide a guideline to properly study and model asphaltene in future studies.

/pdf/critical-review-of-asphaltene-properties-and-factors-3rf85d7438.pdf

Critical review of asphaltene properties and factors impacting its stability in crude oil

https://link.springer.com/content/pdf/10.1007/s12182-020-00467-5.pdf

A review of development methods and EOR technologies for carbonate reservoirs

Experimental and Theoretical Assessment of the Asphaltene Precipitation Characteristics of the Sahil Field Under a Proposed Miscible Gas Injection Scheme

The inertial navigation system (INS) is a basic component to obtain a continuous navigation solution in various applications. The INS suffers from a growing error over time. In particular, its navigation solution depends mainly on the quality and grade of the inertial measurement unit (IMU), which provides the INS with both accelerations and angular rates. However, low-cost small micro-electro-mechanical systems (MEMSs) suffer from huge error sources such as bias, the scale factor, scale factor instability, and highly non-linear noise. Therefore, MEMS-IMU measurements lead to drifts in the solutions when used as a control input to the INS. Accordingly, several approaches have been introduced to model and mitigate the errors associated with the IMU. In this paper, a machine-learning-based adaptive neuro-fuzzy inference system (ML-based-ANFIS) is proposed to leverage the performance of low-grade IMUs in two phases. The first phase was training 50% of the low-grade IMU measurements with a high-end IMU to generate a suitable error model. The second phase involved testing the developed model on the remaining low-grade IMU measurements. A real road trajectory was used to evaluate the performance of the proposed algorithm. The results showed the effectiveness of utilizing the proposed ML-ANFIS algorithm to remove the errors and improve the INS solution compared to the traditional one. An improvement of 70% in the 2D positioning and of 92% in the 2D velocity of the INS solution were attained when the proposed algorithm was applied compared to the traditional INS solution.

https://www.mdpi.com/1424-8220/22/4/1687/pdf?version=1645449761

Ahmed El Mahdi

Papers

Experimental and Theoretical Assessment of the Asphaltene Precipitation Characteristics of the Sahil Field Under a Proposed Miscible Gas Injection Scheme

A Machine Learning Approach for an Improved Inertial Navigation System Solution

Downhole pH Measurement for WBM Contamination Monitoring and Transition Zone Characterization

Geo-steering Long Horizontal Holes In Abu Dhabi Heterogeneous Carbonate Reservoirs

Identification and Characterization of Transition Zones in Tight Carbonates by Downhole Fluid Analysis