/pdf/environmental-comparative-study-of-biosurfactants-production-hesb7k50.pdf

Environmental comparative study of biosurfactants production and optimization using bacterial strains isolated from Egyptian oil fields


 To quantify the uncertainty in reservoir performance, it is common to build ensembles of models that sample the space of possible reservoirs that are consistent with the available data. To evaluate the spread of possible outcomes, simulations experiments are run for each model in the ensemble to calculate for instance recovery factor. The geoscreening workflow is a common way to do this systematically and in a reasonable time. It can work as follows: First, run simulations with simplified physics to calculate recovery factor for every model in the ensemble. Then, use recovery factor (and other quantities) to rank and select representative models for high, medium, and low performance scenarios that can be used for full field simulations.
 In this paper we present an application of the multiscale sequential fully implicit (MS SFI) framework to simulate extremely complex high-resolution models with simplified physics. This enables us to perform fast evaluations of geological uncertainty, such as in the geoscreening workflow. The multiscale SFI method computes each timestep in two steps: First, it solves a nonlinear equation for pressure (and flow). Then, it solves a nonlinear equation for saturations and mole fractions. The pressure equation is solved iteratively using a multiscale approach.
 The MS SFI method has recently been made generally available in a commercial reservoir simulator and can easily be benchmarked with a state-of-the-art fully implicit (FI) method. The MS SFI method was used to successfully simulate a realistic high-resolution geological model in a practical time frame, achieving approximately 10 times speedup in CPU time compared to the FI method. This demonstrates the ability of the MS SFI method to effectively deal with extremely complex models, enabling fast quantification of geological uncertainty with a shorter turnaround time. In many instances the MS SFI method enables simulation of large models at the original geological resolutions without the need for upscaling.
 Finally, we demonstrate how the MS SFI method benefits a geology screening workflow and discuss future use of the MS SFI framework to create fit-for-purpose simulation engines for other workflows.

Multiscale Reservoir Simulation of High-Resolution Models

Abstract In the oil and gas industry, gas lift is a widely used artificial lift method to boost the oil wells production, particularly when the reservoir pressure is low enough to lift the fluid to the surface. Nevertheless, this application used to face several challenges which affected its processes and led to investing more costs to keep its operations running successfully. This paper summarizes a comprehensive study aimed at optimizing the gas lift operations for one of the crucial offshore oil platforms in the Gulf of Suez Petroleum Company (GUPCO) in Egypt, at minimal costs through the application of a holistic transient modeling approach. While the steady state modeling approach is useful to many engineering processes in the O&amp;G industry, it is not suitable for modeling the gas lift systems as it can experience many transient phenomenon such as transferring of the gas injection point to different levels across the well different operations, changing of the valves setting pressure to accommodate the variation in the temperature happened at the well's different production phases, pressure surging incidents that might occur due to the sudden closure of valves, and the presence of slug flow which can be attributed to improper gas lift design or due to the change of the formation fluid characteristics causing drastic changes in the production rates. Accordingly, a transient modeling approach had been applied to resolve all these gas lift dynamic issues. The findings provide valuable insights into the design, operation, and troubleshooting of the overall gas lift systems using multiple scenarios, including redesigning the valves operating pressures, adjusting the gas injection rates, recommending deeper points of injection, exchanging some types of the installed operating valves, advising a cleanup program for both the wellbores &amp; the gas lift mandrels, and detecting the plugged valves that need to be exchanged in the next intervention plans. The project resulted in increasing the net oil gain for the platform under study by 1500 BOPD (35% Gain) after applying some wellbores interventions as recommended by such a holistic modeling approach. The study also includes the cost measures of such services with a focus on finding the most cost-saving method, thus it helped GUPCO reach its production target with minimal efforts &amp; costs. This paper marks an exceptional solution for perfecting the gas lift applications in GUPCO by allowing the simulation of all the dynamic operations of the gas lift systems which helped in solving all the unstable production incidents &amp; the unloading failures that cannot be captured using the customary steady state gas lift modeling approach, thus, it encouraged GUPCO to consider the gas lift transient modeling as a fundamental element in the optimization of all the future gas lift operations. 

35% Oil Increase for Gas Lift Platform Using Transient Modelling Approach

Abstract The West Nile Delta basin is a significant petroleum region in the Mediterranean, hosting numerous gas and condensate fields. These fields consist of a combination of structural and stratigraphic traps with sandstone reservoirs from the Miocene and Pliocene epochs, where turbidite hydrocarbon reservoirs with substantial hydrocarbon volumes are found. This article focuses on the Miocene gas reservoir of the Abu Madi formation in the Abu Qir and Yazzi offshore gas fields, located in the West Nile Delta basin north of Alexandria, Egypt. The study integrates recorded formation pressure testing points and production history of 6 wells that intervened Abu Madi Formation to identify isolated compartments that do not contribute to field production. The analysis revealed three gas reservoirs. The first reservoir, in the North Abu Qir field, has experienced long-term production, leading to pressure depletion and aquifer sweep, reducing the depth of the gas-water contact. The second reservoir, in the Yazzi field, shows some pressure depletion affected by the first reservoir production, but its gas-water contact remains at its initial depth. The third reservoir, in the eastern compartment, exhibits partial pressure depletion. The conceptual model proposes two main concepts for the relationship between the three gas reservoirs. The first concept involves the local structural framework, where the Abu Madi formation trap consists of multiple anticlines, with spill points playing a crucial role in filling hydrocarbons in these anticlines with the same water contact. The second concept considers the impact of stratigraphic facies changes and heterogeneity on transmissibility and permeability, affecting volumetric recovery efficiency. The information was compiled into a 3D reservoir simulation model, concluding that the remaining hydrocarbons must be redistributed. This opens the possibility of hydrocarbon accumulation in partially isolated compartments that need development. Additionally, the interpretation of recorded formation pressure testing points gradients was essential in identifying hydrocarbon fluid types, revealing an oil rim in the gas reservoir in the eastern compartment of the Abu Qir gas field, which altered the well development strategy in that area. 

Unveiling Mediterranean Heterogenous Gas Reservoirs Through Pressure Points Analysis and Reservoir Simulation Modelling

Liquid loading significantly hinders gas production in unconventional shale wells, restricting flow and causing productivity decline. This study presents a novel approach to address this challenge, utilizing thermochemical fluids to generate in situ pressure and heat and effectively mitigating liquid loading issues. Laboratory experiments were conducted using a specially designed flow loop system to evaluate the performance of thermochemical fluids in alleviating liquid loading. The key treatment parameters such as thermochemical volumes, injection rate, number of cycles, and optimum injection time were optimized to improve the removal efficiency. In addition, PIPESIM software (pipe simulation program) was used to validate the effectiveness of the thermochemical approach for removing the liquid loading issue. Both laboratory results and PIPESIM outcomes confirmed the efficiency of thermochemical fluids in handling liquid loading. Removal efficiency of more than 90% can be achieved using thermochemical injection. The liquid removal efficiency increases with the number of cycles due to the generation of more pressure and heat at later injection cycles. Increasing injection cycles from 1 to 3 resulted in liquid removal efficiency rising from 17 to 95%. Also, PIPESIM results indicated that the gas production rate can be improved by around 74% after applying thermochemical treatment. Overall, this study introduces an effective treatment for liquid loading mitigation with significant potential to enhance gas production. The proposed method offers several advantages, including ease of application and extended well life. 

Mitigating Liquid Loading in Gas Wells Using Thermochemical Fluid Injection: An Experimental and Simulation Study


 Reservoir simulation is required to aid in the decision-making for high-impact projects. It is a culmination of geophysical, geological, petrophysical, and engineering assessments of sparse, uncertain, and expensive data. History matching is a process of elevating trust in numerical models as they are calibrated to mimic the behaviour of the real-life asset. Traditional history matching relies on direct parameter assignment based on flat files used as input to the reservoir simulator. This enables a convenient method for the perturbation of uncertain parameters and their value assignments during the history matching process. Given the nature of the input files, the scope for uncertainty parameters is limited to the original petrophysical properties, their derived simulation properties in a specified group of grid blocks, and occasionally extended to include fluid and multiphase flow properties. However, there are key influential model-building steps prior to reservoir simulation related to data interpretation. These steps control not only the values of petrophysical properties but also their spatial correlation, cross-correlation, and variability. The limitation in the scope for parameterization adds bias to the model calibration process, hence negatively impacting its outcome.
 In an era where ML/AI algorithms are shaping data interpretation methods, key modelling decisions can be revisited to realize the maximum value of subsurface data. However, a framework is required whereby these important model-building steps are captured in history matching to eliminate bias and ensure the geological consistency of the subsurface model during and after history matching. This paper demonstrates a liberated workflow to calculate the recommended parameters that achieve the minimum mismatch score. The workflow is executed through a cloud platform offering compute elasticity to expedite the history matching workflow. It is composed of three main steps. The first step is data loading, where simulation results and parameters are extracted from the submitted ensemble(s).
 Meanwhile, the second step involves data preparation and cleaning. Wells devoid of data are removed, and scaled metrics are created to calculate the mismatch score. The simulation ID then groups the data to get a field-level aggregation. The now aggregated and cleaned simulation results are merged with the parameters list to create the input dataset to the final step, where several machine learning models are trained and evaluated in parallel. The data is split into training and testing datasets. The target variable is the mismatch score, as the models are trying to predict the mismatch for a given set of parameters. Supervised learning regression algorithms were used. The best-performing ones were found to be random forest and gradient boosted trees. After fine-tuning the machine learning models and evaluating them based on their coefficient of determination (R2 score), the best fitting model is used to calculate the optimized parameters. This happens iteratively by generating new series of parameters within a range and using the machine learning model to predict the mismatch for each until the lowest mismatch is found. The parameters resulting in the minimum mismatch are the recommended parameters.
 This workflow is implemented on a simulation model built for a mature gas condensate field in the Mediterranean of Egypt. The field comprises three anticlines with a spill-fill Petroleum system, where the majority of the wells are in one of the anticlines. In contrast, the other anticlines have few wells and are candidates for appraisal. Moreover, there is high uncertainty in the sand distribution and reservoir properties, spill points depth, depletion, and observing an explained phenomenon of a sustainable gas water contact in the new anticline even after 30 years of production from the old Anticline. This uncertainty in the understand of the relation between the two anticlines makes the selection of the drilling locations a challenge. To Assess remaining reservoir volumes and identify potential infill targets, we used the ML to study all the uncertainties combinations in a full-loop approach from static to dynamic model and generate multiple representations that honour the geological understanding.
 The cloud-based Agile reservoir modeling approach enriched with ML / AI algorithms enabled us to generate Multiple realizations that match 30 years of historical production and pressure profiles capturing many possible combinations of uncertain geological parameters and concepts. In addition, several forecast scenarios for 3 new appraisal wells were optimized based on the ensemble of history matched models minimizing the risk of drilling dry wells. In addition to going through the work process and results, this paper highlights the method's practical effectiveness and common issues in practical application.
 The use of the cloud-based technology had a great cost saving and efficiency improvements, for example giving the existing on-premises Infrastructure would take 1-2 years to achieve the same results that was achieved in 1-2 months and cost saving around 1 million dollars in cluster hardware purchase. Moreover, Cloud based technology enable collaborative, iterative working styles for integrated teams and access to scalable technologies that are developed on cloud only.

Cloud-Based Agile Reservoir Modelling Enriched with Machine Learning Improved the Opportunity Identification in a Mature Gas Condensate


 In retrograde gas condensate reservoirs, condensate blockage is a major reservoir damage problem, where liquid is dropped-out of natural gas, below dew-point pressure. Despite that most of this liquid will not produce due to not reaching the critical saturation, natural gas will be blocked by the accumulated liquid and will also not produce.
 This work investigates the effects of gas injection (such as methane, carbon-dioxide, and nitrogen) and steam at high temperatures on one of the Egyptian retrograde gas condensate reservoirs. Several gas injection scenarios that comprise different combination of gas injection temperature, enthalpy, injection gas types (CO2, N2, and CH4), and injection-rates were carried out.
 The results indicated that all conventional and thermal gas injection scenarios do not increase the cumulative gas production more than the depletion case. The non-thermal gas injection scenarios increased the cumulative condensate production by 8.6%. However, thermal CO2 injection increased the condensate production cumulative by 28.9%.
 It was observed that thermal gas injection does not vaporize condensate It was observed that thermal gas injection does not vaporize condensate more than conventional injection that have the same reservoir pressure trend. However, thermal injection mainly improves the condensate mobility. Appropriately, thermal injection in retrograde reservoirs, is mostly applicable for depleted reservoirs when the largest amount of non-producible liquid is already dropped out. Finally, this research studied executing thermal gas injection in retrograde gas condensate reservoirs, operationally, by considering the following items: carbon dioxide recovery unit, compressors, storage-tanks, anti-corrosion pipe-lines and tubing-strings, and corrosion-inhibitors along with downhole gas heaters.

Enhancing Hydrocarbon Production Through Thermal Gas Injection from a Retrograde as Condensate Reservoir in the Western Desert in Egypt

Background: Liver donation is a respectable human gift. It is essential that complications should be minimized as much as possible and must be safely performed. Aim of the work: To evaluate feasibility and safety of using CUSA for liver resection in living donor liver transplantation (LDLT) in comparison with harmonic scalpel. Patients and methods: This prospective study included 40 consecutive donors for LDLT held at Nasser Institute and Air Force Specialized Hospital during the period from September 2017 to September 2018. Results: Harmonic scalpel significantly reduced operative time (p=0.000) with a reduced blood loss (p=0.016), however it caused higher rate of biliary leakage (40% vs 15% respectively). On the other hand, CUSA showed lower bile leak leading to shorter hospital stay (p=0.000). Conclusion: Although harmonic scalpel is a faster method, CUSA is more effective and safer with a lower complication rate despite being cumbersome and need a longer learning curve.

Randomized Comparative Study between Using Harmonic Scalpel versus Cavitron Ultrasonic Surgical Aspirator with Bipolar Cautery in Living Donor Hepatectomy for Living Donor Liver Transplantation


 Offshore gas-lifted wells are challenging due to the numerous factors affecting performance, starting from the surface gas compression facility to reservoir performance. Mature oil fields add more challenges due to many flow assurance and mechanical problems. Real-time well monitoring is a must for early problem identification. Also, performance modeling is powerful and helpful for identifying and rectifying problems early. The work here emphasizes an optimization strategy for offshore gas-lifted wells.
 This paper introduces cases of offshore problematic gas-lifted wells and their full optimization and problems solving strategy to be utilized as an integrated approach for solving the problems of similar problematic gas-lifted wells in any field. The recommended strategy depends on studying problematic gas-lifting wells covering some commonly encountered problems. The recommended remedial actions for the selected problematic cases in this intensive study resulted in precious oil gains, cost savings, and gas lift usage optimization. The solution combines surveillance, multiphase simulation, data analytics, and operations.
 This paper discusses three major problems and the strategy to solve them: the first is wells with erroneous surface gas measurement and excessive gas injection; the second is unstable gas-lifted wells, and the third is optimizing low reservoir deliverability gas-lifted wells. In addition, other individual optimization cases, including integrated full-field cases, are introduced for the recommended strategy's completeness. This comprehensive study finds that the optimum approach for rectifying most gas-lifted wells problems must start with real-time monitoring, then modeling the case, and end by recommending possible solution scenarios and their impact on optimizing well performance.
 This study brings the significance of surveillance and dynamic simulations in the overall production cycle: planning to operations. Further, dynamic simulations also help arrive at operators’ guidelines on avoiding failed start-ups and ensuring stable operation. Finally, the power of integration between different disciplines is shown by incorporating several subsurface and surface information in the uncertainty study.

Strategic Modelling Approach for Optimizing and Troubleshooting Gas Lifted Wells: Monitoring, Modelling, Problems Identification and Solutions Recommendations

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Papers

Cloud-Based Agile Reservoir Modelling Enriched with Machine Learning Improved the Opportunity Identification in a Mature Gas Condensate

Enhancing Hydrocarbon Production Through Thermal Gas Injection from a Retrograde as Condensate Reservoir in the Western Desert in Egypt

Randomized Comparative Study between Using Harmonic Scalpel versus Cavitron Ultrasonic Surgical Aspirator with Bipolar Cautery in Living Donor Hepatectomy for Living Donor Liver Transplantation

Strategic Modelling Approach for Optimizing and Troubleshooting Gas Lifted Wells: Monitoring, Modelling, Problems Identification and Solutions Recommendations