authorea Authorea 10.22541/essoar.169447348.83464468/v1 An object-based approach to differentiate pores and microfractures in petrographic analysis using explainable, supervised machine learning 0000-0002-1681-926X Jayachandran Issac Sujay Anand John

Texas A&M University

Gibbs Holly Catherine

Texas A&M University

Laya Juan Carlos

Texas A&M University

Qaiser Yemna

Texas A&M University at Qatar

0000-0002-8217-5562 Khan Talha

Texas A&M University at Qatar

Ansari Mohammed Ishaq

Texas A&M University at Qatar

0000-0002-6335-6923 Ansari Mohammed Yaqoob Mohammed Shoeb

Texas A&M University at Qatar

Malyah Mohammed

Texas A&M University at Qatar

0000-0002-3510-9642 Alyafei Nayef

Department of Petroleum Engineering, Texas A&M University at Qatar

Seers thomas

Texas A&M University at Qatar

11 9 2023 This preprint is available at https://doi.org/10.22541/essoar.169447348.83464468/v1 ai binary classification carbonates geology informatics machine learning petrography pore typing