Strategy for Optimum Chemical Enhanced Oil Recovery Field Operation

The sandstone of Abu Madi clastic reservoir constitutes the prime gas-producing reservoir in the Nile Delta region. So, the present research aims to delineate its microfacies and dominant diagenetic features and their effect on the petrophysical characteristics of the Abu Madi reservoir in the Faraskur Gas Field. This could be achieved by integrating the petrographical studies and the conventional data to divide the reservoir into some reservoir rock types (RRTs) to delineate the impact of the mineral composition and diagenetic features on their reservoir quality and hydraulic flow zones. Petrographically, most samples are immature and described as angular to subrounded, poorly sorted, and slightly cemented. Five clastic microfacies types are recorded in the Abu Madi Formation: (1) quartz arenite, (2) feldspathic quartz arenite, (3) sub-feldspathic quartz arenite, (4) quartz wacke, and (5) sub-feldspathic quartz wacke. The most important post-depositional processes affecting the reservoir quality are the precipitation of pyrite and microsparite, as well as the feldspars alteration into authigenic dispersed clays represented by kaolinite and smectite/illite mixed layers. The dominant pore types are the primary intergranular, with some subsidiary fractures and dissolution pores. To check the reservoir quality of the Abu Madi clastics, the NPI (normalized porosity index), FZI (flow zone indicator), RQI (reservoir quality index), and λk (permeability anisotropy) were estimated. Petrophysically, the studied samples are summed up into four RRTs, with the RRT4 group has the lowest ∅ and k values (av. ∅ = 16.2%, av. kH = 6.87 md) and the lowest reservoir quality parameters (av. FZI = 0.89 μm, av. DRT = 10.4, av. RQI = 0.18 μm, av. R35 = 1.26 μm). On the contrary, the RRT1 group has the best petrophysical and reservoir parameters (av. ∅ = 25.9%, av. kH = 2695 md, av. DRT = 14.9, av. RQI = 3.12 μm, av. FZI = 8.85 μm, av. R35 = 32.7 μm). The permeability anisotropy of the RRT2 and RRT3 is the highest in Faraskur Field (av. λk = 2.4 and 2.62, respectively). This study is applicable to other extensions of the Abu Madi reservoirs in the Nile Delta basin to help in predicting their reservoir quality, petrophysical properties, and their hydrocarbon potentiality.

Petrophysical rock typing based on the digenetic effect of the different microfacies types of Abu Madi clastic reservoir in Faraskur Gas Field, onshore Nile Delta, Egypt

Eysa,

Nabawy,

Ghoneimi

et al. 2023

New insights into estimating the cementation exponent of the tight and deep carbonate pore systems via rigorous numerical strategies

Rostami,

Helalizadeh,

Moghaddam

et al. 2024

One of the main constituents of any reservoir characterization is an accurate forecast of water saturation, which is highly dependent upon the cementation exponent. Even though there have been a lot of studies, the most common correlations depend on total porosity. This means that they do not work as well in heterogeneous carbonate reservoirs, especially tight formations with total porosities less than 10%. This study aims to develop accurate and universal models for estimating the cementation exponent in deep and tight carbonate pore systems located in West Asia. Two heuristic algorithms, including the radial basis function neural network optimized by ant colony optimization (RBFNN-ACO) and gene expression programming (GEP), were employed to calculate the cementation exponent. To do this, we prepared a databank incorporating cementation exponents, total porosity, and various pore types. Then, the databank is classified into the test subset (for model prediction checking) and the train subset (for model construction). The reliability of the new recommended models is inspected by applying several statistical quality measures associated with graphical analyses. So, the consequences of the modeling disclose the large precision of the above-mentioned RBFNN-ACO, GEP Model-I, and GEP Model-II by average absolute percentage relative deviations (AAPRD%) of 6.28%, 6.39%, and 7.45%, respectively. Based on the outliers analysis, nearly 95% of the databank and model estimations are, respectively, valid and reliable. Additionally, the three input variables, including moldic porosity (with a + 70% impact value), non-fabric-selective dissolution (connected) porosity (with a -30% impact value), and interparticle porosity (with a -23% impact value), exhibit the main affecting parameters on the cementation exponent. Comparing current results with traditional literature correlations demonstrates the supremacy of the RBFNN-ACO model (AAPRD = 6.28% and root mean squared error (RMSE) = 0.17) over the examined literature correlations such as Borai’s equation (AAPRD = 12.30% and RMSE = 0.41). In addition, RBFNN-ACO can give better results than Borai’s Eqn. for tight (porosity less than 10%) and deep carbonate samples.

Development of a novel packer fluid for high-temperature and high-pressure oil and gas wells with using design of experiments and artificial intelligence

Mahdavi Kalatehno,

Khamehchi

2024

This study introduces a novel phosphate-based packer fluid, designed for use in high-temperature and high-pressure oil and gas wells. The research aims to evaluate the performance of this innovative fluid in comparison with traditional acetate and formate-based fluids. The study highlights the enhanced performance metrics of the phosphate-based fluid, which include a higher density of 114 pcf, moderated pH levels from 13.5 to 10, and a significantly reduced corrosion rate to below 4 mpy, achieved through the addition of diammonium phosphate and potassium vanadate. Moreover, the research presents two machine learning models (an artificial neural network (ANN) and genetic programming (GP)) developed to predict the penetration depth of the phosphate-based fluid. Both models demonstrate high accuracy, with R-square values of 0.9468 and 0.9140, respectively, with the ANN model exhibiting slightly superior performance. The findings of the study indicate that the phosphate-based fluid, free of solubilizers and enhanced with innovative corrosion inhibitors, provides optimal thermal stability, minimal formation damage, and shallow penetration depth, thus representing a significant advancement in well completion technologies. The fluid’s distinctive properties and the predictive models’ high accuracy highlight its suitability for challenging environments, marking a notable progression in well completion technologies.