Curated by Nora Hamidaddin, Associate Editor SandRose Magazine

In this section, we curate a number of recommendations for technical papers from subject matter experts on topics relating to their respective disciplines.


Pore surfaces of reservoir rocks: Smooth or rough?  And why should we care? 

Proposed by Shouxiang Mark Ma, Sr Petroleum Engineering Consultant, Saudi Aramco

Paper: SPE-201689-PA Objective-Driven Solid-Surface-Roughness Characterization for Enhanced Nuclear-Magnetic-Resonance Petrophysics from SPE Journal  26 (05): 2860–2879

All rock pore geometry models used in the oil and gas industry assume that pore surfaces are smooth. In reality, however, rock surfaces are intuitively rough. How to characterize surface roughness and evaluate its impacts on derived petrophysical properties are the main objectives of this paper, where pore surfaces are characterized by using an extra high-resolution laser scanner confocal microscope. Results indicate that grainy limestones have relatively higher surface roughness compared to muddy limestones and dolostones. There are many applications to the proposed study, and in the paper, the example used illustrates how NMR pore characterization can be enhanced by considering pore surface roughness.

Unconventional resources:

Thermal Maturity Estimation by Raman Spectroscopy for Unconventional Reservoirs

Proposed by Wael Abdallah, SDCR Center Manager, Schlumberger Middle East

Paper: Fast and accurate shale maturity determination by Raman spectroscopy measurement with minimal sample preparation from the International Journal of Coal Geology 173 (PP150-PP157)

Production of petroleum from unconventional reservoirs (UR) is key to meeting future global energy demands. However, it still requires better characterization for profitable exploitation. Accurate knowledge of organic matter thermal maturity allows prediction of the petroleum type in the reservoirs. The paper introduces a robust correlation between Raman signal and thermal maturity for a variety of source rocks with a wide range of maturity developed at Schlumberger Carbonate Research Center. The resulting correlation enables the estimation of thermal maturity expressed as vitrinite reflectance equivalent to being estimated in unknown formation samples. The proposed framework is also applicable on rock samples at the well site with minimum preparation.

Machine Learning / Petrophysics:

Machine Learning Strategies for Accurate Well Logs Prediction

Proposed by Ahmed Abouzaid, Formation Evaluation SME, Baker Hughes

presented at SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition Virtual, October 2021

Artificial Intelligence (AI) or Machine Learning (ML) allows for building automated processes with minimal human intervention and improving the efficiency of well log prediction. The paper compares two different approaches to ML modeling: 1) the self-calibrating model, and 2) the domain-knowledge ML model. The results from the first approach could not achieve satisfactory accuracy because of the complex relationship between petrophysical parameters. The second approach, however, showcased better results, where the input well logs used are the standard logs GR, resistivity, and density-neutron. The proposed framework achieved a prediction accuracy of R2 (regression model R-squared) score of 87% and CC (correlation coefficient) of 96%.

Carbon Management:

Carbon Capture & Storage

Proposed by Ammar Alshehri, General Supervisor at Upstream Carbon and Circularity Division, Saudi Aramco

Paper: Carbon Capture – North America: Reassessing the Opportunity by Morgan Stanley Research

Morgan Stanley recently published a report on carbon capture in North America. The report highlights several aspects of Carbon Capture & Storage (CCS) including major planned projects, existing CO2 infrastructure, and incentive schemes. It also highlights the anticipated CCS cost, where the capture cost has a higher and wider range of $21-$171 per ton of CO2 based on the source of the CO2, while the storage cost has a lower and narrower range of $7-$11 per ton of CO2. The paper provides useful information for benchmarking a topic that is gaining huge interest nationally and worldwide.

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