Evaluation of Formation Damage of Oil-Based Drilling Fluids Weighted with Micronized Ilmenite or Micronized Barite

Ibrahim, Ahmed Farid; Al-Mujalhem, Manayer; Nasr‐El‐Din, Hisham A.; Al‐Bagoury, Mohamed

doi:10.2118/200482-pa

Cited by 12 publications

(3 citation statements)

References 9 publications

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“…In addition, important minerals such as magnesian clays, pyrite and barite were not included as they did not appear in significant quantities in the database. Pyrite is a critical mineral in reservoir characterization for its low resistivities, impacting water saturation calculations by resistivity tools (Hamada & Al‐Awad, 2000; Hamada et al., 2001; Pratama et al., 2017), and the identification of natural barite from the formation aids in the identification of permeable zones damaged by barite‐dense drilling fluid (Bageri et al., 2019; Ibrahim et al., 2017; Ibrahim et al., 2020).…”

Section: Methodsmentioning

confidence: 99%

Hybrid mineral model integrating probabilistic and machine learning approaches for the Brazilian pre‐salt carbonate reservoirs

Oliveira

Freitas

Pesce

et al. 2023

Geophysical Prospecting

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Well mineralogy can be estimated from probabilistic, direct and machine learning models; however, all these models have limitations. The maximum number of components in probabilistic models is restricted to the number of logs plus one. Direct models require the precise composition of minerals. Machine learning models demand unbiased databases, a challenge as the samples are collected in reservoir intervals. These limitations impact the evaluation for the Santos Basin pre‐salt rocks due to the complexity of facies and magnesian clays. This work proposes creating a hybrid model through the combination of probabilistic and machine learning models. First, mineral fractions of calcite, dolomite, quartz, k‐feldspar, detrital clay, plagioclase and pyroxene are estimated by the algorithm XGBoost trained using rock samples. Then, a probabilistic model reconstructs the well logs and machine learning estimations through the seven minerals mentioned plus magnesian clays, pyrite, barite and fluids. The difference between the real and reconstructed responses is minimized, weighted by the curves’ uncertainties. The hybrid model is used to estimate the mineralogy of three wells drilled in the Santos Basin, honouring the mineralogy of the rock samples collected in these wells and improving the quantification of dolomite, pyroxene and magnesian clay. Among the advances introduced, the following stand out: The use of machine learning estimates and well logs improved the quantification of magnesian clay; the machine learning estimates regularized the probabilistic model, generating more coherent results; the uncertainties of the machine learning algorithms dealt with database bias. The hybrid model mitigated limitations related to database bias without the costs associated with collecting more samples.

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Section: Methodsmentioning

confidence: 99%

Hybrid mineral model integrating probabilistic and machine learning approaches for the Brazilian pre‐salt carbonate reservoirs

Oliveira

Freitas

Pesce

et al. 2023

Geophysical Prospecting

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“…Barite, hematite, ilmenite, gelana, and other weighing minerals are utilized in the oil and gas sector 18 – 24 . Furthermore, some of these materials were micronized to improve their properties and reduced related issues such as sagging 17 , 25 – 28 . Manganese tetroxide (Mn 3 O 4 ) was proposed to be used as weighting material, it has a specific gravity of ~ 4.8 g/cm 3 , low particle size around 1 μm, and spherical shape 29 , 30 .…”

Section: Introductionmentioning

confidence: 99%

Improving the filtration properties for manganese tetroxide mud utilizing perlite particles to drill wide-range permeability sandstone formation

Jaberi

Bageri

Elkatatny

2022

Sci Rep

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A required feature of any drilling formulation is to mitigate the formation damage by having an excellent filtration and filter cake properties. The key factor for preventing and limiting formation damage is to improve the sealing qualities of the planned drilling fluid. In this study, a new novel filtration agent called “perlite” was introduced for improving the manganese tetroxide mud cake ability for better sealing features. The perlite particles were loaded to formulation containing the manganese tetroxide as weighting agent. The water-based drilling mud was designed at high densities (14.25 and 17.2 ppg). Perlite was added in varied concentrations to reach the optimum performance. The filtration test conducted at reservoir temperature of 250 °F and a differential pressure of 300 psi to form the filter cake. The tests were performed using sandstone cores with two different permeability categories (low and high permeabilities) as the filtration media. This gave the full picture of perlite performance as implemented for different formation properties and considering the drilling fluid properties. The formed filter cake structure and chemical composition was evaluated using scanning electron energy-dispersive X-ray (SEM–EDS). The presented results illustrated how the perlite was compatible to be added to the manganese tetroxide weighting agents in the same formulation. In addition, it has the capacity to improve the filter cake's sealing qualities, lowering the filtration volume by 41% and the filter cake internal and external layers permeability by 58% and 25%, respectively. Moreover, the EDS analysis showed that the perlite particles are concentrated generally in the internal layer of the filter cake.

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“…ALMALIK, 2001;PRATAMA;ISMAIL;RIDHA, 2017). A identificação de barita natural da formação auxilia na identificação de zonas permo-porosas danificadas pelo fluido de perfuração adensado com baritina (IBRAHIM; SAMI;BALASUBRAMANIAN, 2017;BAGERI et al, 2019;IBRAHIM et al, 2020).…”

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Modelagem geoquímica e mineralógica dos reservatórios carbonáticos do pré-sal da bacia de Santos através de perfis de poços e inteligência artificial.

Oliveira¹

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Evaluation of Formation Damage of Oil-Based Drilling Fluids Weighted with Micronized Ilmenite or Micronized Barite

Cited by 12 publications

References 9 publications

Hybrid mineral model integrating probabilistic and machine learning approaches for the Brazilian pre‐salt carbonate reservoirs

Hybrid mineral model integrating probabilistic and machine learning approaches for the Brazilian pre‐salt carbonate reservoirs

Improving the filtration properties for manganese tetroxide mud utilizing perlite particles to drill wide-range permeability sandstone formation

Modelagem geoquímica e mineralógica dos reservatórios carbonáticos do pré-sal da bacia de Santos através de perfis de poços e inteligência artificial.

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