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.

scholarly journals Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4836
Author(s):  
Liping Zhang ◽  
Yifan Hu ◽  
Qiuhua Tang ◽  
Jie Li ◽  
Zhixiong Li
Keyword(s):  
Decision Making ◽  
Data Base ◽  
Real Time ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Data Driven ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Production Data ◽  
Online Decision Making

In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers’ expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness.

scholarly journals Machine learning to explain printability induced by rheology additives

2021 ◽  
Author(s):  
Ali Nadernezhad ◽  
Jürgen Groll
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Acid Solutions ◽  
Continuous Growth ◽  
Multiple Systems ◽  
Molecular Weights ◽  
Underlying Basis

With the continuous growth of extrusion bioprinting techniques, ink formulations based on rheology modifiers are becoming increasingly popular, as they enable 3D printing of non-printable biologically-favored materials. However, benchmarking and characterization of such systems are inherently complicated due to the variety of rheology modifiers and differences in mechanisms of inducing printability. This study tries to explain induced printability in formulations by incorporating machine learning algorithms that describe the underlying basis for decision-making in classifying a printable formulation. For this purpose, a library of rheological data and printability scores for 180 different formulations of hyaluronic acid solutions with varying molecular weights and concentrations and three rheology modifiers were produced. A feature screening methodology was applied to collect and separate the impactful features, which consisted of physically interpretable and easily measurable properties of formulations. In the final step, all relevant features influencing the model’s output were analyzed by advanced yet explainable statistical methods. The outcome provides a guideline for designing new formulations based on data-driven correlations from multiple systems.

scholarly journals Integrated 3D geomechanical characterization of a reservoir: case study of "Fuja" field, offshore Niger Delta, Southern Nigeria

2021 ◽  
Author(s):  
C. C. Agoha ◽  
A. I. Opara ◽  
O. C. Okeke ◽  
C. N. Okereke ◽  
C. N. Onwubuariri ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Niger Delta ◽  
Unconfined Compressive Strength ◽  
Gamma Ray ◽  
Horizontal Stress ◽  
Vertical Stress ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Geomechanical Characterization

Abstract3D geomechanical characterization of "Fuja" field reservoirs, Niger Delta, was carried out to evaluate the mechanical properties of the reservoir rock which will assist in reducing drilling and exploitation challenges faced by operators. Bulk density, sonic, and gamma-ray logs from four wells were integrated with 3D seismic data and core data from the area to estimate the elastic and inelastic rock properties, pore pressure, total vertical stress, as well as maximum and minimum horizontal stresses within the reservoirs from empirical equations, using Petrel and Microsoft Excel software. 3D geomechanical models of these rock properties and cross-plots showing the relationship between the elastic and inelastic properties were also generated. From the results, Young's modulus, bulk modulus, bulk compressibility, shear modulus, Poisson's ratio, and unconfined compressive strength recorded average values of 5.11 GPa, 5.10 GPa, 0.023 GPa−1$$,$$ , 2.39 GPa, 0.39, and 39.0 GPa, respectively, in the sand, and 6.08 GPa, 6.09 Gpa, 0.016 GPa−1 2.84 GPa, 0.42, and 42.3 GPa, respectively, in shale, implying that the sand is less elastic and ductile and will deform before the shale under similar stress conditions. Results also revealed mean pore pressures of 13,248 psi and 15,220 psi in sand and shale units, respectively, mean total vertical stress of 28,193 psi, mean maximum horizontal stress of 26,237 psi, and mean minimum horizontal stress of 21,532 psi. From the geomechanical models, the rock elastic and inelastic parameters revealed higher values around the northeastern and parts of the eastern and western portions of the reservoir implying that mechanical rock deformation will be minimal in these sections of the field compared to other sections during drilling and post-drilling activities. The generated cross-plots indicate that a relationship exists between the elastic rock properties and unconfined compressive strength. Stress estimations within the reservoirs in relation to the obtained elastic and rock strength parameters show that the reservoirs are stable. These results will be invaluable in mitigating exploration and exploitation challenges.

scholarly journals The Use of Well-Log Data in the Geomechanical Characterization of Middle Cambrian Tight Sandstone Formation: A Case Study from Eastern Pomerania, Poland

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6022
Author(s):  
Małgorzata Słota-Valim ◽  
Anita Lis-Śledziona
Keyword(s):  
Seismic Data ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Hydraulic Fractures ◽  
Well Log ◽  
Tight Sandstone ◽  
Middle Cambrian ◽  
Log Data ◽  
Geomechanical Characterization

Geomechanical characterization plays a key role in optimizing the stimulation treatment of tight reservoir formations. Petrophysical models help classify the reservoir rock as the conventional or unconventional type and determine hydrocarbon-saturated zones. Geomechanical and petrophysical models are fundamentally based on well-log data that provide reliable and high-resolution information, and are used to determine various relationships between measured borehole parameters and modeled physical rock properties in 3D space, with the support of seismic data. This paper presents the geomechanical characterization of the Middle Cambrian (Cm2) sediments from Eastern Pomerania, north Poland. To achieve the aim of this study, 1D well-log-based and 3D models based on seismic data of the rocks’ petrophysical, elastic, and strength properties, as well as numerical methods, were used. The analysis of the Middle Cambrian deposits revealed vertical and horizontal heterogeneity in brittleness, the direction of horizontal stresses, and the fracturing pressure required to initiate hydraulic fractures. The most prone to fracturing is the gas-saturated tight sandstones belonging to the Paradoxides Paradoxissimus formation of Cm2, exhibiting the highest brittleness and highest fracturing pressure necessary to stimulate this unconventional reservoir formation.

Towards the multi-scale characterization of braided fluvial geobodies from outcrop, core, ground-penetrating radar and well log data

2018 ◽  
Vol 488 (1) ◽  
pp. 73-95 ◽  
Author(s):  
Luis Miguel Yeste ◽  
Saturnina Henares ◽  
Neil McDougall ◽  
Fernando García-García ◽  
César Viseras
Keyword(s):  
Ground Penetrating Radar ◽  
Gamma Ray ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Random Pattern ◽  
Log Data ◽  
Architectural Elements ◽  
Ground Penetrating ◽  
Gamma Ray Log

AbstractThe integrated application of advanced visualization techniques – validated against outcrop, core and gamma ray log data – was found to be crucial in characterizing the spatial distribution of fluvial facies and their inherent permeability baffles to a centimetre-scale vertical resolution. An outcrop/behind outcrop workflow was used, combining the sedimentological analysis of a perennial deep braided outcrop with ground-penetrating radar profiles, behind outcrop optical and acoustic borehole imaging, and the analyses of dip tadpoles, core and gamma ray logs. Data from both the surface and subsurface allowed the recognition of two main architectural elements – channels and compound bars – and within the latter to distinguish between the bar head and tail and the cross-bar channel. On the basis of a well-constrained sedimentological framework, a detailed characterization of the gamma ray log pattern in the compound bar allowed several differences between the architectural elements to be identified, despite a general cylindrical trend. A high-resolution tadpole analysis showed that a random pattern prevailed in the channel, whereas in the bar head and tail the tadpoles displayed characteristic patterns that allowed differentiation. The ground-penetrating radar profiles aided the 3D reconstruction of each architectural element. Thus the application of this outcrop/behind outcrop workflow provided a solid database for the characterization of reservoir rock properties from outcrop analogues.

Intelligent Decision Making Approach for Performance Evaluation of a Robot-Based Manufacturing Cell

2018 ◽  
Author(s):  
Tavo Kangru ◽  
Jüri Riives ◽  
Tauno Otto ◽  
Meelis Pohlak ◽  
Kashif Mahmood
Keyword(s):  
Decision Making ◽  
Automobile Industry ◽  
Production System ◽  
High Speed ◽  
Manufacturing Systems ◽  
Production Performance ◽  
Design Parameters ◽  
Manufacturing Cell ◽  
Study Results ◽  
Four Levels

Manufacturing is moving towards complexity, large integration, digitalization and high flexibility. A combination of these characteristics is a basic for forming a new kind of production system, known as Cyber Physical System (CPS). CPS is a board range of complex, multidisciplinary, physically-aware next generation engineered systems that integrates embedded computing technologies. Those integrated manufacturing systems usually consist of four levels: network, enterprise, production system and workplace. In this article we are concentrated to the workplace level, examining the implementation of the most suitable robot-cell and integration it into the production system and enterprise structure. The problem is actual for the big companies such as automobile industry, but very important is also for small and medium sized enterprises (SMEs) that tend to produce for example; small tractors, air conditioners for high speed trains or even different type of doors for houses. In all cases the best solution to response the situation is the implementation of robot-based manufacturing cell into a production system, which is not only a challenge but also need a lot of specific knowledge. Designing and selecting optimal solutions for robot-based manufacturing systems is suitable to carry out by a computer-based decision support systems (DSS). DSS typically works by ranking, sorting or choosing among the alternatives. This article emphasis to the problem of integration the DSS with the artificial intelligence (AI) tools. For this objective, the study has been focused to development of a conceptual model for assessing robot-based system by means of technical and functional capabilities, which is combined with cell efficiency based on process Key Performance Indicators (KPIs) and enterprise Critical Success Factors (CSFs). The elaborated model takes into consideration system design parameters, product specific indicators, process execution data, production performance parameters and estimates how the production cell objective can be achieved. Ten different types of companies were selected and their robot-based manufacturing systems were mapped by qualitative and quantitative factors based on the model, whereas executives were interviewed to determine companies’ strategic objectives. The study results comprise of an approach that helps SMEs to gain additional economic-technical information for decision making at different levels of a company.

Sign in / Sign up

Export Citation Format

Share Document