Implementation of a Petrographical and Petrophysical Workflow Protocol for Studying the Impact of Heterogeneity on the Rock Typing and Reservoir Quality of Reefal Limestone:A Case Study on the Nullipore Carbonates in the Gulf of Suez

This study focuses on the heterogeneity of the middle Miocene syn-rift Belayim nullipore(reefal)marine sequences in the Gulf of Suez and its impacts on reservoir quality.The sequences consist of coralline algal reef limestones with a highly complex dual-porosity system of primary and secondary porosities of widely varying percentages.To achieve a precise mathematical modeling of these reservoir sequences,a workflow protocol was applied to separate these sequences into a number of hydraulic flow units(HFUs)and reservoir rock types(RRTs).This has been achieved by conducting a conventional core analysis on the nullipore marine sequence.To illustrate the heterogeneity of the nullipore reservoir,the Dykstra-Parsons coefficient(V)has been estimated(V=0.91),indicating an extremely heterogeneous reservoir.A slight to high anisotropy(λ_(k))has been assigned for the studied nullipore sequences.A stratigraphic modified Lorenz plot(SMLP)was applied to define the optimum number of HFUs and barriers/baffles in each of the studied wells.Integrating the permeability-porosity,reservoir quality index-normalized porosity index(RQI-NPI)and the RQI-flow zone indicator(RQIFZI)plots,the discrete rock types(DRT)and the R35 techniques enable the discrimination of the reservoir sequences into 4 RRTs/HFUs.The RRT4 packstone samples are characterized by the best reservoir properties(moderate permeability anisotropy,with a good-to-fair reservoir quality index),whereas the RRT1 mudstone samples have the lowest flow and storage capacities,as well as the tightest reservoir quality.

Applications of artificial intelligence in geothermal resource exploration: A review

Artificial intelligence (AI) has become increasingly important in geothermal exploration,significantly improving the efficiency of resource identification.This review examines current AI applications,focusing on the algorithms used,the challenges addressed,and the opportunities created.In addition,the review highlights the growth of machine learning applications in geothermal exploration over the past decade,demonstrating how AI has improved the analysis of subsurface data to identify potential resources.AI techniques such as neural networks,support vector machines,and decision trees are used to estimate subsurface temperatures,predict rock and fluid properties,and identify optimal drilling locations.In particular,neural networks are the most widely used technique,further contributing to improved exploration efficiency.However,the widespread adoption of AI in geothermal exploration is hindered by challenges,such as data accessibility,data quality,and the need for tailored data science training for industry professionals.Furthermore,the review emphasizes the importance of data engineering methodologies,data scaling,and standardization to enable the development of accurate and generalizable AI models for geothermal exploration.It is concluded that the integration of AI into geothermal exploration holds great promise for accelerating the development of geothermal energy resources.By effectively addressing key challenges and leveraging AI technologies,the geothermal industry can unlock cost‐effective and sustainable power generation opportunities.

Unconventional resource's production under desorption-induced effects

Thousands of horizontal wells are drilled into the shale formations across the U.S.and hydrocarbon production is substantially increased during past years.This fact is accredited to advances obtained in hydraulic fracturing and pad drilling technologies.The contribution of shale rock surface desorption to production is widely accepted and confirmed by laboratory and field evidences.Nevertheless,the subsequent changes in porosity and permeability due to desorption combined with hydraulic fracture closures caused by increased net effective rock stress state,have not been captured in current shale modeling and simulation.Hence,it is essential to investigate the effects of induced permeability,porosity,and stress by desorption on ultimate hydrocarbon recovery.We have developed a numerical model to study the effect of changes in porosity,permeability and compaction on four major U.S.shale formations considering their Langmuir isotherm desorption behavior.These resources include;Marcellus,New Albany,Barnett and Haynesville Shales.First,we introduced a model that is a physical transport of single-phase gas flow in shale porous rock.Later,the governing equations are implemented into a one-dimensional numerical model and solved using a fully implicit solution method.It is found that the natural gas production is substantially affected by desorption-induced porosity/permeability changes and geomechancis.This paper provides valuable insights into accurate modeling of unconventional reservoirs that is more significant when an even small correction to the future production prediction can enormously contribute to the U.S.economy.