Numerical Simulation of Magnesium Dust Dispersion and Explosion in 20 L Apparatus via an Euler–Lagrange Method

Computational fluid dynamics (CFD) was used to investigate the explosion characteristics of a Mg/air mixture in a 20 L apparatus via an Euler–Lagrange method. Various fluid properties, namely pressure field, velocity field, turbulence intensity, and the degree of particle dispersion, were obtained and analyzed. The simulation results suggested that the best delayed ignition time was 60 ms after dust dispersion, which was consistent with the optimum delayed ignition time adopted by experimental apparatus. These results indicate that the simulated Mg particles were evenly diffused in the 20 L apparatus under the effect of the turbulence. The simulations also reveal that the pressure development in the explosion system can be divided into the pressure rising stage, the maximum pressure stage, and pressure attenuation stage. The relative error of the maximum explosion pressure between the simulation and the experiments is approximately 1.04%. The explosion model provides reliable and useful information for investigating Mg explosions.

Quantitative assessment of areal geological complexity

Background. The article presents a new approach to assessing the geological complexity — quantitative assessment of areal complexity, as well as an alternative methodology for assessing complexity in 1D. Aim. Developing a numerical metric for assessing the geological complexity and creating an algorithm for complexity maps construction. Materials and methods. Generally, complexity describe the reservoir in one number, that often underestimates the real complexity of the deposit. Geological complexity, presented in the article consists of 5 groups: structuraltectonic, facies-lithological, permeability and porosity, secondary alteration and fluid properties, 13 characteristics describe the complexity space of these groups. Each of these characteristics could be presented not only in 1D but also in 2D. The proposed methodology was tested on the company’s assets. Results. The presented examples of complexity maps for several fields show the advantage of 2D complexity estimation in comparison with 1D. The analysis of decomposed complexity estimation (for individual groups) on the company’s assets showed that the key groups of complexity are structural-tectonic, facies-lithological characteristics. Therefore, characteristics that describe these groups should be taken into account during the decision-making process and assets ranking. Conclusion. A methodology of quantitative assessment of areal geological complexity has been developed. This areal assessment allows identify the most “problematic” areas, analyzing existing sources of uncertainty, and also ranking and screening company assets when making strategic decisions.

A Cognitive Data-Driven Single-Well Modeling Workflow for Reservoir Deliverability Predictions – Expanding the Wireline Formation Tester Application Envelope

In today's dynamically challenging E&P industry, exploration activities demand for out-of-the-box measures to make the most out of the data available at hand. Instead of relying on time consuming and cost-intensive deliverability testing, there is a strong push to extract maximum possible information from time- and cost-efficient wireline formation testers in combination with other openhole logs to get critical reservoir insight. Consequently, driving efficiency in the appraisal process by reducing redundant expenditures linked with reservoir evaluation. Employing a data-driven approach, this paper addresses the need to build single-well analytical models that combines knowledge of core data, petrophysical evaluation and reservoir fluid properties. Resultantly, predictive analysis using cognitive processes to determine multilayer productivity for an exploratory well is achieved. Single Well Predictive Modeling (SWPM) workflow is developed for this case which utilizes plethora of formation evaluation information which traditionally resides across siloed disciplines. A tailor-made workflow has been implemented which goes beyond the conventional formation tester deliverables while incorporating PVT and numerical simulation methodologies. Stage one involved reservoir characterization utilizing Interval Pressure Transient Testing (IPTT) done through the mini-DST operation on wireline formation tester. Stage two concerns the use of analytical modeling to yield exact solution to an approximate problem whose end-product is an estimate of the Absolute Open Flow Potential (AOFP). Stage three involves utilizing fluid properties from downhole fluid samples and integrating with core, OH logs, and IPTT answer products to yield a calibrated SWPM model, which includes development of a 1D petrophysical model. Additionally, this stage produces a 3D simulation model to yield a reservoir production performance deliverable which considers variable rock typing through neural network analysis. Ultimately, stage four combines the preceding analysis to develop a wellbore production model which aids in optimizing completion strategies. The application of this data-driven and cognitive technique has helped the operator in evaluating the potential of the reservoir early-on to aid in the decision-making process for further investments. An exhaustive workflow is in place that can be adopted for informed reservoir deliverability modeling in case of early well-life evaluations.

Development of New Models to Determine the Rheological Parameters of Water-based Drilling Fluid Using Artificial Intelligence

It is well known that drilling fluid is a key parameter for optimizing drilling operations, cleaning the hole, and managing the rig hydraulics and margins of surge and swab pressures. Although the experimental works present valid and reliable results, they are expensive and time consuming. On the other hand, continuous and regular determination of the rheological mud properties can perform its essential functions during well construction. More uncertainties in planning the drilling fluid properties meant that more challenges may be exposed during drilling operations. This study presents two predictive techniques, multiple regression analysis (MRA) and artificial neural networks (ANNs), to determine the rheological properties of water-based drilling fluid based on other simple measurable properties. While mud density (MW), marsh funnel (MF), and solid% are key input parameters in this study, the output functions or models are plastic viscosity (PV), yield point (YP), apparent viscosity (AV), and gel strength. The prediction methods were demonstrated by means of a field case in eastern Iraq, using datasets from daily drilling reports of two wells in addition to the laboratory measurements. To test the performance ability of the developed models, two error-based metrics (determination coefficient R2 and root mean square error RMSE) have been used in this study. The current results of this study support the evidence that MW, MF, and solid% are consistent indexes for the prediction of rheological properties. Both mud density and solid content have a relative-significant effect on increasing PV, YP, AV, and gel strength. However, a scattering around each fit curve is observed which proved that one rheological property alone is not sufficient to estimate other properties. The results also reveal that both MRA and ANN are conservative in estimating the fluid rheological properties, but ANN is more precise than MRA. Eight empirical mathematical models with high performance capacity have been developed in this study to determine the rheological fluid properties based on simple and quick equipment as mud balance and marsh funnel. This study presents cost-effective models to determine the rheological fluid properties for future well planning in Iraqi oil fields.

Liquid Metal-Based Devices: Material Properties, Fabrication and Functionalities

This paper reviews the material properties, fabrication and functionalities of liquid metal-based devices. In modern wireless communication technology, adaptability and versatility have become attractive features of any communication device. Compared with traditional conductors such as copper, the flow characteristics and lack of elastic limit of conductive fluids make them ideal alternatives for applications such as flexible circuits, soft electronic devices, wearable stretch sensors, and reconfigurable antennas. These fluid properties also allow for innovative manufacturing techniques such as 3-D printing, injecting or spraying conductive fluids on rigid/flexible substrates. Compared with traditional high-frequency switching methods, liquid metal (LM) can easily use micropumps or an electrochemically controlled capillary method to achieve reconfigurability of the device. The movement of LM over a large physical dimension enhances the reconfigurable state of the antenna, without depending on nonlinear materials or mechanisms. When LM is applied to wearable devices and sensors such as electronic skins (e-skins) and strain sensors, it consistently exhibits mechanical fatigue resistance and can maintain good electrical stability under a certain degree of stretching. When LM is used in microwave devices and paired with elastic linings such as polydimethylsiloxane (PDMS), the shape and size of the devices can be changed according to actual needs to meet the requirements of flexibility and a multistate frequency band. In this work, we discuss the material properties, fabrication and functionalities of LM.

An Innovative Methodology for Estimating Rock Mechanical Properties from Weight or Volume Fractions of Mineralogy and its Application to Middle East Reservoirs

The influences of mineralogy on rock mechanical properties have profound application in oil and gas exploration and production processes, including hydraulic fracturing operations. In conventional resources, the rock mechanical properties are predominantly controlled by porosity; however, in unconventional tight formations, the importance of mineralogy as a function of rock mechanical properties has not been fully investigated. In unconventional tight formations, mechanical properties are often derived from mineralogy weight fraction together with the best estimate of porosity, assumption of fluid types, the extent of pore fillings, and fluid properties. These properties are then adjusted for their volumetric fractions and subsequently calibrated with acoustics or geomechanical lab measurements. A new method is presented that utilizes mineralogy weight fractions (determined from well logs or laboratory measurements). This process uses public domain information of minerals using Voigt and Reuss averaging algorithms as upper and lower bounds, respectively. An average of these bounds (also known as Hill average) provides a representative value for these parameters. Further, based on isotropic conditions, all the elastic properties are calculated. A typical output consisting of bulk-, shear-, and Young's - modulus, together with Poisson's ratio obtained from traditional methods of volume fractions and this new method using weight fractions is discussed and analyzed along with the sensitivity and the trends for individual rock properties. Furthermore, corresponding strengths, hardness, and fracture toughness could also be estimated using well known public domain algorithms. Data from carbonate reservoirs has been discussed in this work. This method shows how to estimate grain compressibility that can be challenging to be measured in the lab for unconventional tight rock samples. In low-porosity samples, the relative influence of porosity is negligible compared to the mineralogy composition. This approach reduces several assumptions and uncertainties associated with accurate porosity determination in tight rocks as it does not require the amount of pore fluids and fluid properties in calculations. The grain-compressibility and bulk-compressibility (measured by hydrostatic tests in the laboratory on core plugs or calculated from density and cross-dipole log) are used to calculate poroelastic Biot's coefficient, as this coefficient will be used to calculate in-situ principal effective stresses (overburden, minimum horizontal, and maximum horizontal stresses), which are, together with rock properties and pore pressure, constitutes the geomechanical model. The geomechanical model is used for drilling, completions, and hydraulic fracture modeling, including wellbore stability, and reservoir integrity analyses.

Full-Field History-Matching of Commingling Stacked Reservoirs: A Case Study of an Oman Southern Asset

This paper presents an innovative and practical workflow framework implemented in an Oman southern asset. The asset consists of three isolated accumulations or fields or structures that differ in rock and fluid properties. Each structure has multiple stacked members of Gharif and Alkhlata formations. Oil production started in 1986, with more than 60 commingling wells. The accumulations are not only structurally and stratigraphically complicated but also dynamically complex with numerous input uncertainties. It was impossible to assist the history matching process using a modern optimization-based technique due to the structural complexities of the reservoirs and magnitudes of the uncertain parameters. A structured history-matching approach, Stratigraphic Method (SM), was adopted and guided by suitable subsurface physics by adjusting multi-uncertain parameters simultaneously within the uncertainty envelope to mimic the model response. An essential step in this method is the preliminary analysis, which involved integrating various geological and engineering data to understand the reservoir behavior and the physics controlling the reservoir dynamics. The first step in history-matching these models was to adjust the critical water saturation to correct the numerical water production by honoring the capillary-gravity equilibrium and reservoir fluid flow dynamics. The significance of adjusting the critical water saturation before modifying other parameters and the causes of this numerical water production is discussed. Subsequently, the other major uncertain parameters were identified and modified, while a localized adjustment was avoided except in two wells. This local change was guided by a streamlined technique to ensure minimal model modification and retain geological realism. Overall, acceptable model calibration results were achieved. The history-matching framework's novelty is how the numerical water production was controlled above the transition zone and how the reservoir dynamics were understood from the limited data.