Schlumberger (Norway)

Decisions regarding problem conceptualization, search approach, and how best to parametrize optimization methods for practical application are key to successful implementation of optimization approaches within georesources field development projects. This work provides decision support regarding the application of derivative-free search approaches for concurrent optimization of inflow control valves (ICVs) and well controls. A set of state-of-the-art approaches possessing different search features is implemented over two reference cases, and their performance, resource requirements, and specific method configurations are compared across multiple problem formulations for completion design. In this study, problem formulations to optimize completion design comprise fixed ICVs and piecewise-constant well controls. The design is optimized by several derivative-free methodologies relying on parallel pattern-search (tAPPS), population-based stochastic sampling (tPSO) and trust-region interpolation-based models (tDFTR). These methodologies are tested on a heterogeneous two-dimensional case and on a realistic case based on a section of the Olympus benchmark model. Three problem formulations are applied in both cases, i.e., one formulation optimizes ICV settings only, while two joint configurations also treat producer and injector controls as variables. Various method parametrizations across the range of cases and problem formulations exploit the different search features to improve convergence, achieve final objectives and infer response surface features. The scope of this particular study treats only deterministic problem formulations. Results outline performance trade-offs between parallelizable algorithms (tAPPS, tPSO) with high total runtime search efficiency and the local-search trust-region approach (tDFTR) providing effective objective gains for a low number of cost function evaluations. tAPPS demonstrates robust performance across different problem formulations that can support exploration efforts, e.g., during a pre-drill design phase while multiple independent tDFTR runs can provide local tuning capability around established solutions in a time-constrained post-drill setting. Additional remarks regarding joint completion design optimization, comparison metrics, and relative algorithm performance given the varying problem formulations are also made.

In this paper the application of centrifugal type gas separator as a part of submersible electric screw pumping unit is considered. A number of bench tests have been carried out to study the centrifugal gas separator on viscous gas-liquid mixture. Calculation of the dependence of delivery on the developed pressure of a single screw pump at the angular speed of rotation of the rotor 225 rpm for different gas content has been made. Also, characteristic curves of gas separators of groups 5 and 5A at different gas contents and different viscosity of gas-liquid mixture are obtained. A methodology for bench testing of gas separators has been developed, which takes into account the influence of gas-liquid mixture properties, changes in the angular speed of the electric motor shaft rotation, and natural separation parameters. The operating parameters of a gas separator of group 5A were determined experimentally, taking into account a residual gas content of 50%, in the range of angular speeds of 252 -1000 rpm (4.2 -16.7 Hz).

Summary Thermal enhanced oil recovery (TEOR) is the most widely accepted method for exploiting the heavy oil reservoirs in North America. In addition to improving the mobility of oil due to its viscosity reduction, the high temperature down in the hole due to the injection of the vapor phase may significantly alter the fluid flow performance and behavior, as represented by the relative permeability to fluids in the formations. Therefore, in TEOR, the relative permeabilities can change with a change in temperature. Also, there is no model that accounts for the change in temperature on two-phase gas/oil relative permeability. Further, the gas/oil relative permeability and its dependence on temperature are required data for the numerical simulation of TEOR. Very few studies are available on this topic with no emerging consensus on a general behavior of such effects. The scarcity of such studies is mostly due to experimental problems to make reliable measurements. Therefore, the primary objective of this study was to overcome the experimental issues and investigate the effect of temperature on gas/oil relative permeability. Oil displacement tests were carried out in a 45-cm-long sandpack at temperatures ranging from 64°C to 210°C using a viscous mineral oil (PAO-100), deionized water, and nitrogen gas. It was found that the unsteady-state method was susceptible to several experimental artifacts in viscous oil systems due to a very adverse mobility ratio. However, despite such experimental artifacts, a careful analysis of the displacement data led to obtaining meaningful two-phase gas/oil relative permeability curves. These curves were used to interpret the relative permeability curves for gas/heavy oil systems using the experimentally obtained displacement results. We noted that at the end of gasflooding, the “final” residual oil saturation (Sor) still eluded us even after several pore volumes (PVs) of gas injection. This rendered the experimentally determined endpoint gas relative permeability (krge) and Sor unreliable. In contrast, the irreducible water saturation (Swir) and the endpoint oil relative permeability (kroe) were experimentally achievable. The complete two-phase gas/heavy oil relative permeability curves are inferred with a newly developed systematic history-matching algorithm in this study. This systematic history-matching technique helped us to determine the uncertain parameters of the oil/gas relative permeability curves, such as the two exponents of the Corey equation (No and Ng), Sor and krge. The history match showed that kroe and Swir were experimentally achievable and were reliably interpreted, except these four parameters (i.e., Corey exponents, true residual oil saturation, and gas endpoint relative permeability) were interpreted from simulations rather than from experiments. Based on our findings, a new correlation has been proposed to model the effect of temperature on two-phase gas/heavy oil relative permeability.

Abstract The giant Rumaila oilfield in south Iraq has been plagued by a number of wellbore instabilities. The shaley formations in this field pose the greatest challenges to drilling operations, accounting for approximately 90% of the problems. Stuck pipe, tight hole, and borehole collapse are examples of the incidents commonly encountered even while drilling vertical wells. The main objectives of this study were to generate a mud weight window cube for different trajectory scenarios by examining the impact of depletion on field performance and drilling integrity for making life-of-reservoir decisions. The derived mechanical properties were taken from their respective 1D mechanical earth models (MEM) by utilizing the available mechanical core test data and populating in 3D space. The final 3D MEM has been calibrated against field observations. This paper presents a well planning tool for translating the geomechanical modeling results to operational parameters, and in so doing, informing well trajectory planning and optimization. Introduction Reservoir geomechanics play a vitally important role in minimizing drilling activity costs and informing field development planning. Comprehensive geomechanical models (1D, 3D and 4D) have been built in the Rumaila Field based on a wide array of inputs taken from 14 wells distributed across the field. The analysis examined the stress changes in the main reservoirs—namely: Mishrif and Zubair Main Pay—and addressed the instability issues in all Rumaila formations encountered while drilling, by using the available openhole logs, core mechanical test data, production data, and pressure data. The main objectives of this study were to build a fieldscale geomechanical model with a view to assessing the impact of production-induced stress changes on field performance and generating mud weight cubes at different timesteps (Al-Asadi 2021).

Abstract On 30 October 2020, an Mw 6.9 normal faulting earthquake occurred off the northern coasts of Samos Island, Greece. The earthquake with moderate magnitude generated the largest tsunami in the eastern Mediterranean since the 1956 Amorgos event. Here, we investigate the earthquake source characteristics and examine the factors contributing to the exacerbated tsunami using an integrated approach, including the source inversion from InSAR and GPS data, tsunami simulation, and spectral analysis of tsunami waveforms. The results show that a set of conjugate faults, determined in this study, can explain the geodetic surface deformation equally well. Our tsunami modeling results indicate that the shallow north‐dipping fault was most likely responsible for the mainshock. With the key features of our preferred source models, we infer that the tsunami was intensified by several conditions: (a) the spatial distribution of major coseismic slips coincided with the deepest portion of the Samos Basin which served as an energy reservoir for the tsunami generation; (b) the tsunami was generated in a semi‐closed bay which trapped most of the tsunami energy; (c) the east‐west orientation of the mainshock fault and the location of the concentrated coseismic slips directed the majority of the tsunami energy toward Sığacık Bay, which formed the extremely high tsunami of 3.8 m; (d) the primary tsunami wave period, close to natural oscillation frequency of the harbor, resulted in the observed significant oscillation. We conclude that the tsunami of the 2020 Samos earthquake was exacerbated by both the source characteristics and tsunami resonance property.

The current research addressed the topic of social resilience and the response to the COVID-19 pandemic in both Egypt and the United Arab Emirates on a comparative basis. The research formulated a group of theatrical issues, put several goals and questions, and developed a methodological design based on combining the requirements of quantitative and qualitative research, starting with the re-analysis approach to results of literature, analyzing the available statistics and secondary data, as well as employing the participation observation tool for situations and events related to the pandemic. The research came out with several results, including the necessity to search for alternatives to create transformational resilience in preparation for future pandemics, building corporate social responsibility on the principle of humanity to achieve social justice for all, shedding light on social resilience in the family system in response to pandemics, and adding the trust variable to other variables of social resilience.

Abstract Seismic fault detection using machine-learning techniques, in particular the convolution neural network (CNN), is becoming a widely accepted practice in the field of seismic interpretation. Machine-learning algorithms are trained to mimic the capabilities of an experienced interpreter by recognizing patterns within seismic data and classifying them. Regardless of the method of seismic fault detection, interpretation or extraction of 3D fault representations from edge evidence or fault probability volumes is routine. Extracted fault representations are important to the understanding of the subsurface geology and are a critical input to upstream workflows including structural framework definition, static reservoir and petroleum system modeling, and well planning and derisking activities. Efforts to automate the detection and extraction of geologic features from seismic data have evolved in line with advances in computer algorithms, hardware, and machine-learning techniques. We have developed an assisted fault interpretation workflow for seismic fault detection and extraction, demonstrated through a case study from the Groningen gas field of the Upper Permian, Dutch Rotliegend; a heavily faulted, subsalt gas field located onshore, northeast Netherlands. Supervised using interpreter-led labeling, we apply a 2D multi-CNN to detect faults within a 3D prestack depth migrated seismic data set. After prediction, we apply a geometric evaluation of predicted faults, using a principal component analysis to produce geometric attribute representations (strike azimuth and planarity) of the fault prediction. Strike azimuth and planarity attributes are used to validate and automatically extract consistent 3D fault geometries, providing geologic context to the interpreter and input to dependent workflows more efficiently.

Abstract The Venturi tube, a classic single-phase flow meter, proved to be a reliable and accurate wet gas flow meter, but it requires correction. The presence of liquid content increases the measured differential pressure across the Venturi tube and causes over-reading (OR). Developing novel methods to correct the Venturi tube readings has become a target in the oil & gas industry to quantify production from individual wells. This work is proposing a new approach to overcome the OR challenge using machine learning (ML) models. The ML model to predict OR was developed using the random forest (RF) technique. Initially, synthetic dataset of producing wells were generated. A variation on the type of gasses and liquids were studied including nitrogen and benzene, argon and water, natural gas and water and natural gas and decan. The input parameters consist of fluid properties and fluid conditions. The fluid properties are including gas and liquid phases. The target for these inputs is to predict the OR. The results were then evaluated based on root-mean-square error (RMSE), and the fitness of the model was assessed by the coefficient of determination R2. The ML model showed high accuracy results for the OR prediction of the testing data of R2=0.997 and RMSE of 0.7%. The developed model was then applied to a new set of data of air and water for further validation. This validation resulted in R2= 0.998 and RMSE of 0.5%. This shows that the RF technique is capable of predicting the OR in wet gas when the aforementioned input parameters are used. The uniqueness of this ML model is that the inputs are all measurable in the field. Once the OR is predicted, the "real" gas mass flow rate can be calculated directly. The novelty of this work lies in providing a robust method to calculate the "real" gas mass flow rate real-time which ultimately diminishes the need to use published correlations derived from experimental conditions that are not necessarily representative of the oil field conditions.