In	contrast	to	the	well-studied	North	Sea	chalks,	the	effectiveness	of	log-based	specific	surface	area	modeling	techniques	
for	heterogeneous	Iranian	(Middle	Eastern)	carbonates	is	underexplored.Petrographic	analysis	exhibits	processes	like	dissolution,	dolomitization,	
and	anhydrite	cementation	that	have	significantly	altered	the	Asmari	Formation's	fabric,	leading	to	substantial	pore	struc
ture	heterogeneity.Results	are	compared	with	core	data,	including	
porosity,	permeability,	facies	identified	through	thin-section	petrography,	mineralogy	via	X-ray	diffraction,	mercury	injec
tion	porosimetry,	and	low-field	nuclear	magnetic	resonance	spectrometry.The	first	method	utilizes	density	and	gamma
ray	logs,	as	previously	validated	in	North	Sea	chalks,	while	the	second	method	innovatively	integrates	deep	resistivity	
and	porosity	data	using	a	K-nearest	neighbor	machine	learning	algorithm.Provided	the	fractures	in	the	core	samples	truly	represent	fractures	in	
the	reservoir,	permeability	modeled	using	log-based	specific	surface	tends	to	underestimate	permeability,	achieving	a	cor
relation	coefficient	of	0.62	for	the	first	method	and	an	improved	results	(CC	=	0.7)	from	the	machine-learning	approach