Petrophysical reservoir-rock properties and source-rock characterization of Abu Roash Formation in Wadi El-Rayan oil field, Western Desert, Egypt

2018 ◽  
Vol 11 (14) ◽  
Author(s):  
Mohamed Afife ◽  
Ralf Littke ◽  
Aref Lashin ◽  
Mohamed Abdel All
Keyword(s):  
Source Rock ◽  
Oil Field ◽  
Reservoir Rock ◽  
Western Desert ◽  
Rock Properties ◽  
Rock Characterization

Application of Computational Intelligence in Generating Synthetic Reservoir Rock Mechanical Parameters for Building Geo-Models.

2015 ◽  
Author(s):  
L. C. Akubue ◽  
A.. Dosunmu ◽  
F. T. Beka
Keyword(s):  
Neural Network ◽  
Statistical Analysis ◽  
Numerical Models ◽  
Petroleum Industry ◽  
Oil Field ◽  
Wellbore Stability ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Earth Model ◽  
Sonic Log

Abstract Oil field Operations such as wellbore stability Management and variety of other activities in the upstream petroleum industry require geo-mechanical models for their analysis. Sometimes, the required subsurface measurements used to estimate rock parameters for building such models are unavailable. On this premise, past studies have offered variety of methods and investigative techniques such as empirical correlations, statistical analysis and numerical models to generate these data from available information. However, the complexity of the relationships that exists between the natural occurring variables make the aforementioned techniques limited. This work involves the application of Artificial Neural Networks (ANNs) to generating rock properties. A three-layer back-propagation neural network model was applied predicting pseudo-sonic data using conventional wireline log data as input. Four well data from a Niger-Delta field were used in this study, one for training, one for validating and the two others for generating and testing results. The network was trained with different sets of initial random weights and biases using various learning algorithms. Root mean square error (RMSE) and correlation coefficient (CC) were used as key performance indicators. This Neural-Network-Generated-Sonic-log was compared with those generated with existing correlations and statistical analysis. The results showed that the most influential input vectors with various configurations for predicting sonic log were Depth-Resistivity-Gamma ray-Density (with correlating coefficient between 0.7 and 0.9). The generated sonic was subsequently used to compute for other elastic properties needed to build mechanical earth model for evaluating the strength properties of drilled formations, hence optimise drilling performance. The models are useful in Minimizing well cost, as well as reducing Non Productive Time (NPT) caused by wellbore instability. This technique is particularly useful for mature fields, especially in situations where obtaining this well logs are usually not practicable.

Geochemical and petrophysical source rock characterization of the Vaca Muerta Formation, Argentina: Implications for unconventional petroleum resource estimations

2017 ◽  
Vol 184 ◽  
pp. 27-41 ◽  
Author(s):  
Maria-Fernanda Romero-Sarmiento ◽  
Sebastian Ramiro-Ramirez ◽  
Guillaume Berthe ◽  
Marc Fleury ◽  
Ralf Littke
Keyword(s):  
Source Rock ◽  
Petroleum Resource ◽  
Vaca Muerta ◽  
Rock Characterization ◽  
Vaca Muerta Formation

scholarly journals Analysis of the Petroleum System of Dima Oil Field by Using PetroMoD 1D

2021 ◽  
pp. 526-531
Author(s):  
Haider A. F. Al-Tarim
Keyword(s):  
Source Rock ◽  
Oil And Gas ◽  
Petroleum System ◽  
Oil Field ◽  
Reservoir Rock ◽  
Geological Time ◽  
Reservoir Rocks ◽  
Petroleum Systems ◽  
Geological Age ◽  
Stratigraphic Succession

The study of petroleum systems by using the PetroMoD 1D software is one of the most prominent ways to reduce risks in the exploration of oil and gas by ensuring the existence of hydrocarbons before drilling.      The petroleum system model was designed for Dima-1 well by inserting several parameters into the software, which included the stratigraphic succession of the formations penetrating the well, the depths of the upper parts of these formations, and the thickness of each formation. In addition, other related parameters were investigated, such as lithology, geological age, periods of sedimentation, periods of erosion or non-deposition, nature of units (source or reservoir rocks), total organic carbon (TOC), hydrogen index (HI) ratio of source rock units, temperature of both surface and formations as they are available, and well-bottom temperature.      Through analyzing the models by the evaluation of the source rock units, the petrophysical properties of reservoir rock units, and thermal gradation with the depth during the geological time, it became possible to clarify the elements and processes of the petroleum system of the field of Dima. It could be stated that Nahr Umr, Zubair, and Sulaiy formations represent the petroleum system elements of Dima-1 well.

Petrophysical rock typing in unconventional shale plays: The Niobrara Formation case study

2019 ◽  
Vol 7 (4) ◽  
pp. SJ7-SJ22
Author(s):  
Asm Kamruzzaman ◽  
Manika Prasad ◽  
Stephen Sonnenberg
Keyword(s):  
Source Rock ◽  
Large Scale ◽  
The United States ◽  
Hydrocarbon Exploration ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Small Scale ◽  
Niobrara Formation ◽  
Rock Types ◽  
Rock Quality

Production from organic-rich shale petroleum systems is extremely challenging due to the complex rock and flow characteristics. An accurate characterization of shale reservoir rock properties would positively impact hydrocarbon exploration and production planning. We integrate large-scale geologic components with small-scale petrophysical rock properties to categorize distinct rock types in low-porosity and low-permeability shales. We then use this workflow to distinguish three rock types in the reservoir interval of the Niobrara Shale in the Denver Basin of the United States: the Upper Chalks (A, B, and C Chalk), the Marls (A, B, and C Marl), and the Lower Chalks (D Chalk and Fort Hays Limestone). In our study area, we find that the Upper Chalk has better reservoir-rock quality, moderate source-rock potential, stiffer rocks, and a higher fraction of compliant micro- and nanopores. On the other hand, the Marls have moderate reservoir-rock quality and a higher source-rock potential. The Upper Chalks and the Marls should have major economic potential. The Lower Chalk has higher porosity and a higher fraction of micro- and nanopores; however, it exhibits poor source-rock potential. The measured core data indicate large mineralogy, organic richness, and porosity heterogeneities throughout the Niobrara interval at all scales.

Data-Driven Characterization of Shale Reservoirs Towards Facilitation of Production Performance Evaluation

2021 ◽  
Author(s):  
Shadi Salahshoor
Keyword(s):  
Decision Making ◽  
Production Performance ◽  
Reservoir Rock ◽  
Data Driven ◽  
Rock Properties ◽  
Design Parameters ◽  
Production Data ◽  
Area Of Interest ◽  
Clustering Model

Abstract Leveraging publicly available data is a crucial stepfor decision making around investing in the development of any new unconventional asset.Published reports of production performance along with accurate petrophysical and geological characterization of the areashelp operators to evaluate the economics and risk profiles of the new opportunities. A data-driven workflow can facilitate this process and make it less biased by enabling the agnostic analysis of the data as the first step. In this work, several machine learning algorithms are briefly explained and compared in terms of their application in the development of a production evaluation tool for a targetreservoir. Random forest, selected after evaluating several models, is deployed as a predictive model thatincorporates geological characterization and petrophysical data along with production metricsinto the production performance assessment workflow. Considering the influence of the completion design parameters on the well production performance, this workflow also facilitates evaluation of several completion strategies toimprove decision making around the best-performing completion size. Data used in this study include petrophysical parameters collected from publicly available core data, completion and production metrics, and the geological characteristics of theNiobrara formation in the Powder River Basin. Historical periodic production data are used as indicators of the productivity in a certain area in the data-driven model. This model, after training and evaluation, is deployed to predict the productivity of non-producing regions within the area of interest to help with selecting the most prolific sections for drilling the future wells. Tornado plots are provided to demonstrate the key performance driversin each focused area. A supervised fuzzy clustering model is also utilized to automate the rock quality analyses for identifying the "sweet spots" in a reservoir. The output of this model is a sweet-spot map that is generated through evaluating multiple reservoir rock properties spatially. This map assists with combining all different reservoir rock properties into a single exhibition that indicates the average "reservoir quality"of the formation in different areas. Niobrara shale is used as a case study in this work to demonstrate how the proposed workflow is applied on a selected reservoir formation whit enough historical production data available.

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