Modeling and Simulation of Natural Gas Production from Unconventional Shale Reservoirs

Modeling and simulation of unconventional reservoirs are much more complicated than the conventional reservoir modeling, because of their complex flow characteristics. Mechanisms, which control the flow in the reservoir, are still under the investigation of researchers. However, it is important to investigate applications of mechanisms which are present to our knowledge. This paper presents the theory and applications of flow mechanisms in unconventional reservoir modeling. It is a well-known fact that most of the reservoir flow problems are non-linear due to pressure dependency of particular parameters. It is also widely accepted that fully numerical solutions are costly both computational and time wise. Therefore, the presented model in this paper follows semi-analytical solution methods. Gas adsorption in unconventional reservoirs is the major pressure dependent mechanism;in addition existence of natural fractures is also taken considerable attention. This paper aims to investigate combined effect of existence of natural fractures gas adsorption, and gas slippage effect while keeping the computational effort in acceptable range. Unlike the existing literature (Langmuir is widely used), BET multi-layer isotherm employed in this paper for gas adsorption modeling. A modified dual porosity modeling is used for natural fracture and gas slippage effect modeling. For model verification purposes a history matched is performed with real field data from Marcellus shale. The proposed model in this paper shows a good agreement with the field data. It is observed that BET isotherm models early time production performance more accurately than Langmuir isotherm. It is also concluded that gas adsorption significantly improves the production performances of unconventional reservoirs, with natural fractures. In addition, gas slippage has a slight effect in long term production.

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.

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.