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CONTENTS 4 Foresight 6 Interview
With Prof. Dr. Yasser Mostafa - Director of EPRI
8 a tiered approach
for maximizing the value of noisy seismic data
12 SEASON in REVIEW 14 ENSEMBLE METHODS: An Intelligent Modeling Approach
18 ENHANCED OIL RECOVERY and its applications in heavy oil Egyptian fields
21 DISCOVER IPGC 22 OUR TEAM Feb 2020
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FORESIGHT
Embracing Limitations Do you seek perfection? Does everything you do need to be flawless and without the possibility of failure? If so, your pursuit of perfection might reveal a prominent imperfection!
Ahmed Nabil SCOPE Chairperson Chapter President
There are times when we need to be perfect or near-perfect, such as when constructing a bridge or performing surgery. Falling short of exacting specifications can spell danger. But for the majority of us most of the time, there is room for a margin of error. An attachment to being perfect demonstrates a lack of self-compassion and wisdom. The failure to embrace our humanity with its joys, sorrows and imperfections leads to a rigid sense of self that shatters easily whenever we fall short of our goals. Our emotional health requires gentleness toward ourselves as we embrace inevitable limits. We can find satisfaction in doing our best, but this doesn’t mean that we need to be perfect. How often have we faced the sorrow of making a bad investment, whether in the stock market, relationships or when buying a consumer product? We’re not omnipotent. We can’t see every possible consequence to our actions. For me, making a decision based upon the best info we have is the best because we can’t control life with all of its complexity. “Start where you are. Use what you have. Do what you can.” - Arthur Ashe Joining EAGE Suez 3 years ago was my first step to start exploring my skills. Once you become a member in this entity you will feel the community spirit which leads you to the kickoff. You will know how the real world is when you start to be part of teams and meet new team members. EAGE Suez is the place to gain and share knowledge, empower people to learn more and better understand the world. Here, your passion to leave your mark will be your engine to reach your limits and exceed them. Leading this great team in its sixth season under the slogan ‘’ Break the Anchor’’ was one of the most remarkable challenges I had in my life. It was an honor for me to be a leader among all these creative minds and great spirits. On behalf of EAGE Suez team, I would like to introduce the fourth issue of Scope magazine, the official magazine of EAGE Suez University. I would like to extend my appreciation for the amazing team and their work done to come up with this amazing edition of Scope. The endless hours that you have spent working on this magazine, and the professionalism that you have shown have been a great representative for EAGE Suez vision. When I was elected during the summer to be EAGE Suez President, I had high expectations for what this team can accomplish. Your diligence, self-motivation as well as dedication have been a source of inspiration to the rest of the team. EAGE Suez is always an inspiration for every member to gain glory and experience. We encourage them to be an effective part of the team and make sure that they will explore their hidden abilities and make the best exploitation of them. We never let them rest ‘til his good is better and their better is best. If I am asked for my advice to you, without any hesitation it will be to make your own luck and the only way to do this is to believe in yourself, have faith in your own abilities, work hard and then you can Embrace Limitations.
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FORESIGHT
THE BIGGER PICTURE Do you think you know it all? Is everything really as it seems? I personally think that these two questions need to be asked more often. The way we look at each individual incident, action, or person is different among different people, which leads to each one forming a different judgement on a situation and taking a different action.
Omar Khaled SCOPE CEO Chapter Vice President
Perspective... The word that contains the solution to man’s modern-day conflicts. If we all just think for a minute differently about a single situation, we can eventually find the word “perspective”. To reach a point where one can see something from different points of view at the same time, and form a better, wiser, and more efficient judgement on a situation, which will lead to a more effective solution to any given problem. I can give numerous examples for situations where I didn’t form a good judgement, didn’t think of the right decision, and didn’t take the best action (situations where I didn’t have “perspective”). However, the process I have gone through while taking each of these decisions only renewed my passion for learning and improving my way of thinking. It became clear to me that the more shots you get wrong, the more likely that you will get your next one right. This idea also applies to failures and rough circumstances. If you don’t see the message beyond failures or sudden changes that happen to you on a daily basis, your only hope of moving forward is lost already, and you only have yourself to blame. The way this world works is simple; you move forward, or you stop. Always try to find the good behind the bad, and you need to make peace with the fact that time will not stop for you. “Don’t dwell on your weaknesses, everybody else is already doing that for you.” - Gary Vaynerchuck After my journey with EAGE Suez so far, I cannot stress more on the truth of these words. It has been known that with every step you take forward, you will find those who blame you for even taking that step, even if it was the best step you could take at the moment. On the way, you will not meet many people who will understand and support you, and this is just the way things are. Remember that it is never too late to start, but your future self will thank you if you start now! Without further ado, I would like to invite you to take a tour inside what I like to call “The Green World”. This magazine represents all our values, efforts, and achievements towards a better community, a community with its sights straight forward, a community with dreams of a better world.. a GREEN community. I would like to thank our dedicated team that brought this dream to a reality, and the brilliant minds that supported us through the way. Welcome to SCOPE.
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INTERVIEW
INTERVIEW WITH Prof. DR. YASSER MOSTAFA Director of the Egyptian Petroleum Research Institute (EPRI) analysis services for the whole Egyptian petroleum sector for many years until now. Moreover, we have offered extensive workshops for African professionals. This was a start for more future cooperation on the African scale, and we are turning our focus to more protocols and partnerships on the Arabian scale as well.
3- Who is Dr. Yasser Mostafa? Please tell us about your journey and how you became EPRI Director. I graduated in 1985 from Chemistry department at the faculty of Science, Cairo University. After graduation, I worked first at Al-Nasr Company for Pharmaceutical Industries for six months, then started my career at EPRI in 1986 as an Analysis and Evaluation Specialist. My enthusiasm for scientific research, and for chemistry as I might say, has been with me since high school. It has driven me to specialize in chemistry in the first place, and to continue my path afterwards to receive my master’s degree and then the PhD. I was lucky to have exceptionally supportive supervisors, whose efforts I still appreciate to this day. The course of my life completely changed after travelling for a workshop in Germany for 10 months, may I say they felt like 10 Years!
1. Good Evening, Dr. Yasser, thank you for having us here. Firstly, as anyone may ask, what actually is the “Egyptian Petroleum Research Institute” and what are its goals and vision? Certainly, the Egyptian Petroleum Research Institute is a governmental entity subsidiary of the Ministry of Higher Education and Scientific Research, and its CEO is the Minister of Petroleum. “EPRI” was established in 1974, so we are talking about more than 40 years of experience. Our aim is to serve the petroleum sector and provide it with studies, consultations and innovative solutions. Speaking of innovation, we have applied the concept of innovation in “EPRI” as part of the Egyptian Vision for 2030: “A Creative, Innovative Community”. This can be seen through setting the Institute up with the latest models of unique instruments and establishing a balance between both the academic and practical aims of the Institute.
2. What are the key past achievements of EPRI? And what are your upcoming projects? As a remarkable part of the nation’s industrial and educational system, we have recently been interested in Knowledge Transfer Technology (KTT). As for past accomplishments, we have participated in most of the recent national projects, like providing training for laboratory personnel from ENI, providing analysis results for the Egyptian subway lines’ soil, also we have been providing PVT and core
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After coming back, I was chosen to be in the team working at the new central laboratory. Positions followed one after another, until the 24th of July 2019 when I was assigned as the Director of the Egyptian Petroleum Research Institute. I always say that titles are just words on paper, we are all here to work for our nation’s benefit without regard to names or positions.
4- How do you see the future of the petroleum industry in Egypt? As we can all witness in Zohr field operations, you will notice that we are moving forward. With blessings of God and through the outstanding efforts from national and foreign personnel, we have become one of the exporting (not importing) countries in terms of gas production. This is definitely a promising step towards a better future for the industry in Egypt. Another point I would like to talk about is maximizing the added value. We do not want to keep producing and selling raw materials, just to buy its final products. This has become a recent focus of the Egyptian Refining Company (ERC), which began to handle produced oil and gas and turn them into more valuable products, again maximizing the added value for our market. This is also the current direction for the petrochemicals industry, which will witness a significant upgrade in the coming days.
INTERVIEW
According to the vision of H.E., the Minister of Petroleum and Mineral Resources, it is not enough that we export natural gas (which is an excellent leverage itself), we need to make the most use of our resources, by transforming each pound we can make by selling natural gas into ten or twenty pounds through producing more complex, valuable products. This itself can provide a remarkable rise in our economy, through increasing our production, combating unemployment, and providing more opportunities for our youth. Even if this can bear some cost, we are the winners eventually, as we will turn into a productive community and reach self-sufficiency.
5- How can students participate in fulfilling the Institute’s vision? At the beginning, it works the other way around. We cooperate with the students to support them in fulfilling their message until graduation. Our role as a scientific facility is to train, qualify, and provide the updated knowledge to students, through providing consultations and internships in our labs, and assist them with all technical matters. When undergraduates realize the level and quality of our operations, we secure a brand for the institute among young professionals. This leads to graduates developing a connection to the institute and becoming supporters of its values.
6- What is your opinion on the student activities in the Egyptian universities, specially EAGE Suez? Of course, I am glad of the presence and efforts of such activities. We, as an entity, cannot keep a close eye on young audience of the universities. What gives me comfort is hearing about any events organized by student activities, specially EAGE. When I heard about your recent achievements and events, I realized how you let the students achieve a state of commitment, diligence and responsibility. Your efforts in spreading scientific and cultural awareness is indeed an important step on the path of realizing our shared vision of Egypt 2030. It is critical that we all speak one language, through words or actions, so you offer us a great help through your targets and visions. You also organize conferences, which leads to an increase in the quality of the technical content presented to students, and an upgrade in their mindset regarding presentation and personal skills. It has become a fact that language, personal skills and engagement with industry are three core means for reaching the optimal shape of the Egyptian youth who will lead the nation to its promised destination.
We always long for the validation of our efforts through international verified certificates and providing internship opportunities. This leads to the audience related to the industry hearing about EPRI and its services. Here, we maintain a balance between the quality and quantity of published papers, which is not an easy task, after years of pursuing only the quality side, which led to relative incompetence between staff members before. It is a fact that competition forces an upgrade in technical and personal values, which in general leads to a raise in Egypt’s rank in the scientific field.
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Articles
A tiered approach for maximising the value of a noisy 4D seismic data in a carbonate reservoir
Wong Lee Jean Doctor of Engineering - Heriot-Watt University
Abstract: Despite the perceived challenges, 4D technology has added value in development of the hard carbonate reservoirs in Campos basin. However, extracting such value from 4D seismic data in Field-X was not a trivial task. Very poor repeatability in this field cast doubt on the 4D interpretation driven solely by seismic data analysis. To maximise the added value of the time-lapse data in development of the field and constraining the simulation model, we followed the tiered approach proposed by Amini & MacBeth (2018). Here, several tools were used to assess the reservoir connectivity based on the production data. The 4D seismic response was also examined through modelling and inversion exercises. Assimilation of the production data and the refined 4D attributes enabled us to design a metric to assign a confidence flag to each of the 4D anomalies. This increased the value of information derived from a noisy dataset, as well as the confidence level towards its interpretability.
Introduction: Field-X is a carbonate (limestone) oil reservoir under water-flood located in Campos basin offshore Brazil. Following the drop of the reservoir pressure below bubble point, producing gas-oil-ratio increased rapidly and resulted in reduction of the oil production rate. To arrest the continual decline in productivity, water was injected into the reservoir after 20 years of production. This pressure maintenance programme stabilized the oil production rate and producing GOR, but simultaneously accelerated water production in a number of producers. Despite the perceived challenges, 4D technology has added value in development of the hard carbonate reservoirs in Campos basin. Therefore, 4D seismic data has been acquired in Field-X to monitor the water-flood programme. However, extracting such value from the 4D seismic data in this field is not a trivial task. The baseline seismic survey was acquired 3 years after water injection (23 years after start of the production), and the monitor seismic survey was acquired 9 years later to determine the contributing zones of water production. The 4D seismic data suffers from high levels of non-repeatable noise (40% NRMS). In the undershoot region that covers half of the area of interest, the data is even noisier with NRMS of 60%.
Very poor repeatability in Field-X cast doubt on the 4D interpretation driven solely by seismic data analysis. To maximise the added value of the time-lapse data in constraining the simulation model and development of the field, we followed “the 4D-dedicated tiered approach for time-lapse data interpretation� proposed by Amini & MacBeth (2018). In this approach (Figure 1), a systematic tiered setup is proposed to assimilate the insight about the reservoir quality and connectivity retrieved from 1) production data, 2) seismic data, and the 3) reservoir model. In Tier 1 of this three tier setup, reservoir quality and connectivity is characterised through these three disciplines independently to avoid any bias across different disciplines. In Tier 2, the three disciplines are paired to integrate the findings and uncertainties that were flagged in the previous stage. In Tier 2A, the 4D seismic data is tied to production data. The hardening and softening signals are linked to the reservoir drive mechanism and pressure and saturation changes supported by production data. Tier 2B refers to conventional production history matching. While being highly uncertain, the model provides a detailed picture of the reservoir vertical and lateral heterogeneity and connectivity. Tier 2C indicates seismic to reservoir links. For example, simulator-to-seismic modelling (sim2seis) sheds light on the band-limited seismic signature of the saturation and pressure changes to assure the 4D attributes extraction is representative of the true dynamic reservoir changes. Tier 2 data analysis provides a clear picture of the disparities in characterisation of the reservoir from point of view of the three different disciplines. This enables us to build a geologically consistent consensus over the genuine and false 4D anomalies, to flag the areas of uncertainties and to gain confidence on the added value of the 4D seismic. By passing through several decision making gates in Tier 1 and 2, we can appropriately assist the seismic history matching (ASHM) process in Tier 3. Amini & MacBeth also emphasised that effective communication between the disciplines and co-visualisation are critical in this approach. In Field-X, we have progressed differently on each tier to
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Articles date, which enabled us to create a metric to assign a confidence flag to the 4D seismic anomalies. The results of Tier 1C and 2B are not presented here.
Tier 1A: Reservoir connectivity and quality evaluation based on production data:
Here, reservoir connectivity and production performance was assessed using five different methods (Table 1). To consolidate our findings from stage 1A, the preferential flow paths are delineated through inter-well connectivity quality lines (Figure 2e).
here) to devise our mapping strategy. Sim2seis modelling allowed us to adjust the window width for mapping and to identify the intervals that were mostly affected by wavelet artefacts. The main production zone being from top reservoir, we found that 4D response from top half (above blue horizon) of the reservoir is more likely to be genuine, while bottom half of the reservoir (below blue horizon) was populated with wavelet artefacts. We created acoustic impedance cubes through model-based seismic inversion which reduced the wavelet artefacts to some extent (Figure 4b – circle 2). Through linear regression with
Figure 2 (a) Well chronology: sequence of water breakthroughs derived from water cut for producers, (b) Well interaction: comparison of WCUT between wells to indicate well connectivity, (c) Identification of water production problems from water-cut (WCUT) – Gas-oil-ratio (GOR) trends analysis and (d) Water-oil-ratio (WOR) plots (Chan, 1995) (e) map shows summary of the production data analysis.
Tier 1B: 4D seismic data analysis:
Post-stack 4D processing (cross-equalisation and timeshift correction) was applied prior to extraction of the 4D attributes. In our polarity convention, the peak and trough on the 4D quadrature section (monitor-baseline) represent hardening and softening respectively (Figure 4c). Seismic reservoir characterisation was challenged by low vertical resolution (dominant frequency of 12 Hz) at reservoir level. The reservoir zone on the baseline seismic is made of one or two seismic cycles. We performed 1D seismic modelling to identify the signature of the water-flood (Figure 3). While the peak in track 7 represents the water-flooded interval, the neighbouring troughs (highlighted by arrows) are wavelet artefacts and are not associated with softening. This highlights the uncertainty of interpretation of the 4D sections and map-based attribute extraction. We used sim2seis modelling (not shown
acoustic impedance, a porosity cube was also created (Figure 4c). The reduction of the wavelet artefacts (Figure 4a – circle 1) is also evident by comparing the baseline quadrature section with inversion driven porosity cube.
Tier 2A: Integration of production and seismic data
Several notable anomalies (A to E) are highlighted on the 4D map (Figure 5b). Assimilation of this map and the production data (Figure 2e) allowed us to assign a confidence flag to each of these anomalies. Wells in the centre of the reservoir might be experiencing channelling (WOR analysis) as they are located in the most porous part of the reservoir. This justifies the hardening effect observed on 4D impedance map (area A). This is also consistent with the area of maximum cumulative volume of water production (Wells 18, 27, 34, 35 in Figure 5a). While we acknowledge the high noise level in this dataset, the 4D
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Figure 3 Modelled 1D time-lapse seismic signature of water-flood (OWC rise)
Figure 4 Vertical section (a) quadrature baseline (b) 4D quadrature (monitor - baseline) (c) porosity (d) 4D impedance
section reveals some details about the signature of the water-flood which is consistent with our Tier 1A production data assessment. WOR plots suggested Well 35 and Well 29 to experience channelling and coning respectively. Well 35 seems to be supported from injector Well 23, whereas the hardening signal around Well 29 is indicative of bottom driven aquifer support. The strong softening effect around the injectors (area B) agrees with our observation that the magnitude of incremental pressure is highest at the injectors (the bar plots in Figure 5a). In the undershoot area (area C), the cluster of hardening anomalies are sporadic, localized and discontinuous, even though the porosity distribution seem to be rather continuous. In addition, the incremental volume of produced water is negligible (Wells 28, 29, 32, 33) and the expected amount of pressure support is relatively low. Likewise, areas near to the original oil-water contact (area D) close to Wells 16 and 30 with insignificant water production, are likely to be drained prior to water injection. Therefore a low confidence flag is assigned to this anomaly. The strong coherent softening signal in area E could not be justified since the Wells 25 and 28 experience relatively lower pressure build-up being away from the injectors in the west.
duction data, seismic data and reservoir model, followed by the paired integration of these discipline allowed us to identify the pitfalls of interpreting the data solely driven by seismic analysis. Our analyses indicate that the softening and hardening signal around the injectors and producers in the centre are likely to be genuine, whereas the 4D anomalies within the undershoot area and in the north of the reservoir are less likely to be production induced.
Conclusion:
Field-X development is challenged by many uncertainties associated with a noisy and low-resolution time-lapse seismic dataset. However, our tiered and integrated approach has increased the value of information derived from such dataset, as well as the confidence level towards its interpretability. The independent assessment of pro-
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Figure 5 (a) Bubble map of produced water volumes and pressure increment on a porosity map (b) 4D impedance map with flagged confidence areas.
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Articles
Ensemble Methods: An Intelligent Modelling Approach for Oil & Gas Industry
Saurabh Tewari Senior Research Fellow
Introduction: Ensemble methods have recently gained popularity in reservoir characterization and drilling automation. The petroleum industry is always in search for newer intelligent models in the quest of higher prediction accuracy. Even a marginal improvement in the prediction accuracy may have a large positive impact on the success of oil and gas operations. Estimation of reservoir properties or drilling parameters, with a good degree of accuracy, is still a challenging task for engineers due to the high level of uncertainty, large inexactness, noise, and high dimensionality associated with all sensory drilling data or well logs. Ensemble methods are the latest development in the field of machine learning paradigms for pattern recognition and big data analytics. Ensemble methods are especially found suitable for fulfilling the requirements of petroleum data analytics viz. feature selection, incremental learning for real-time streaming data, the fusion of heterogeneous data from different sources, handling imbalanced data, learning from the non-stationary environment, etc. Ensemble methods are lesser known intelligent models in petroleum domain due to which limited research works are available in the literature. However, they provide solutions to several intelligent modeling issues as briefly summarized below.
Decisions of base learners can be combined together using several combination strategies such as majority voting rule, weighted majority voting, Algebraic combiners, Borda count, etc. (Zhang and Ma, 2012). Supervised algorithms are found to be more stable in the sense of memory plasticity dilemma issue when used as base learners of ensemble methods. Ensemble approach can be implemented in several ways such as random selection or sampling of input training data and feature space, manipulation of an error function, etc. Several ensemble methods have been reported with diverse architectures in the literature such as Bagging, Random forest, Random subspace, Stacked generalization or Stacking, a-mixture-of-experts, Vote, Adaboost, etc. Ensemble methods reduce bias and variance components of prediction error and improve the overall generalization accuracy. Most of the real-time petroleum data are senor generated data that are nonlinear, multidimensional, noisy and imbalanced in nature. Figure 1 shows the diversity existing in different types of petroleum data. Two popular ensemble methods are briefly explained below with their multifarious applications in the Petroleum industry.
Ensemble Methods: Ensemble methods are motivated by real-life practical circumstances where the decisions of experts are better than a single expert for a particular task. It combines the decision of several supervised learners such as Classification and Regression tree (CART), Artificial neural networks (ANNs), Support vector machine (SVM), etc. together to achieve higher classification and regression accuracy. Ensemble methods reduce bias and variance of input sensor data that have a direct influence on prediction accuracy. It trains several supervise learners simultaneously on a fixed bias of input data and then combines the decisions in such a way that reduces the associated variance of data while information content remains unaffected. It has been mathematically proved that multiple-learner-system viz. Ensemble methods are superior to a single learner system such as ANNs, SVM, CART, etc. Ensemble methods are made up of three main segments viz. data selection (diversity in data), training of base learners and combining decision of base learners.
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Figure 1-Big data reservoir collected from different sources (Tewari at al., 2019).
Bagging: Bagging ensemble is also known as Bootstrap
Aggregating because it combines the benefits of boosting and aggregating techniques (Breiman et al., 1996). It generates random independent samples with replacement utilizing the boosting technique for simultaneously training of base learners (Skurichina et al., 2002). Later, decisions of base learners are combined together using various aggregating techniques viz. majority voting rule or averaging the decisions of base learners. Bagging splits the training data into smaller training subsets or bootstrap samples to train the base learners’ paradigms that reduces
Articles Several applications of Bagging ensemble are found in the areas of reservoir characterization, lithofacies identification, reservoir recovery factor estimation, hydrocarbon production estimation, rate of penetration, etc.
Random forest: Random forest is a prolongation of
Bagging ensemble. Random forest algorithm has certain advantages such as handling of prediction and classification tasks, lesser training and fast prediction time, less number of tuning parameters, built-in assessment of generalization error, easy processing of high dimensional data, found suitable for small as well as large datasets, etc. Random forest utilizes decision trees as a base learner for pattern recognition problems. It constructs a swarm of decision trees using bootstrap samples during the training phase and outputs the average prediction of decision trees. It also provides a good estimate of prediction error as an out-of-bag error and error rate (Breiman et al., 2001).
reservoir recovery factor. Training samples were considered randomly after feature extraction to generate 10 sets of training (70%) and testing (30%) datasets using 10-fold cross-validation technique to reduce the chances of overfitting. The performance of machine learning models depends upon the quality, reliability and amount of training data. In the model development layer, processed input reservoir data were applied for training and testing of Ensemble methods viz. Bagging and Random forest. A cluster of hundred support vector regression (SVR) were utilized as base learner’s algorithm in Bagging ensemble. The hyperplane parameters of SVR were optimized separately using an optimization algorithm to obtain the generalized performance of Ensemble methodology. However, a cluster of hundred Decision trees (C4.5) was used as base learners for Random forest in place of SVRs. The performance of the proposed module was evaluated
Figure 2—Ensemble Estimator Module (EEM) based on Bagging ensemble for estimation of the optimum value of weight on bit (WOB) for real-time drilling operations (Tewari, 2019).
Bagging Ensemble Based Framework for drilling WOB estimation: Figure 2 shows an example of a developed EEM module based on Bagging ensemble for the estimation of the optimum values of WOB along with depth. The proposed module consists of three computational layers viz. the preprocessing layer, model development layer and post-processing layer. In preprocessing layer, resampling was done to eradicate any data sample having missing values. Surface drilling data such as round per minutes, torque, standpipe pressure, mud properties, etc. are utilized in this module as input raw data for hydrocarbon recovery factor estimation. Further, resampled data was normalized to reduce the dominance of samples having large values. After normalization, Haar wavelet was utilized to reduce the noise associated with reservoir data followed by feature extraction using Relief algorithm. Relief algorithm also helps to understand the relevance and contribution of each input predictor variable for the estimation of reservoir recovery factor. It allocates rank and importance predictor weight to each reservoir variable according to their contributions in pattern recognition of
using three statistical parameters as given below. Root Mean Square Error (RMSE), Mean Absolute Percentage Error(MAPE) and Coefficient of Correlation (R^2). R2 is a good measure of the estimation capability of machine learning estimators. Value of R2 varies from 0 to 1, where value nearer to one indicates good estimation results. RMSE and MAPE are widely accepted error evaluating criteria for estimation model and utilized in findings. In post-processing layer, the optimally tuned Ensemble model was tested on unseen or new data samples to evaluate its effectiveness and robustness. The optimum model parameters were saved during model development layer. The optimized EEM module can be employed to predict the optimal WOB for Real-time drilling operation. The real-field reservoir data utilized in the research were found to be complex, nonlinear, noisy, and highly dimensional in nature. Heterogeneous nature of hydrocarbon reservoir contributes to complexity, nonlinearly and uncertainty in all the reservoir measurements. However, there are no standard tools or techniques available in the present scenario that can measure the
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Articles reservoir heterogeneity or its influence on other reservoir properties and drilling variables. Therefore, sensory drilling surface data, have been used as variables in the input data for the prediction of drilling WOB.
Conclusions: 1 Ensemble methods are less known intelligent modeling
approach in the petroleum industry and have limited research publications existing in the literature.
2 Ensemble methods have outperformed ANNs and SVM
Areas of Application: Ensemble methods are applied in several areas of Petroleum Engineering for regression and classification tasks. Ensemble methods have been utilized for the estimation of drilling rate of penetration, recognition of subsurface lithofacies, estimation of reservoir recovery factor, reservoir characterization, production optimization, history matching, reservoir parameters estimation, crude oil properties prediction, mud properties prediction, etc.
in several petroleum applications. Supervised learners are found to be more stable as base learners in Ensemble methods.
3 Prediction errors generated due to bias and variance of petroleum data are significantly reduced by Ensemble methods.
4 Ensemble methods are found to be a potential candidate for solving problems of the oil & gas industry.
5 Extended the scope of multidisciplinary research to meet the challenges of hydrocarbon reservoir drilling and modeling.
Figure 3 Block diagram representation of Real-time field application of EEM for drilling rate of Penetration controller.
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Enhanced Oil Recovery Applications in Heavy Oil Egyptian Fields Mohamed Sebaie Introduction: Oil Recovery Methods: Three different recovery methods (Primary, Secondary, or Tertiary) are used in hydrocarbon reservoirs as shown in Fig 1: 1 Primary recovery 2 Secondary recovery 3 Tertiary recovery
I
MSc - Cairo University
with the conventional and light oil, which raise the countries to search for new technologies to produce this heavy oil.
Fig 2. World oil demand and supply according to BP Statistical Review of World Energy 2018.
We focus here on the EOR methods to recover high viscous oil (Heavy Oil) from Egyptian fields, which have already been implemented and which could be implemented in the future. Fig 1. Oil recovery classifications according to the Oil and Gas Journal biennial surveys.
1 Primary recovery: We produce the hydrocarbon in this stage by the nature energy of the reservoir. 2 Secondary recovery: We produce the hydrocarbon in this stage by injecting fluids (Water or Gas) into the formation through injection wells to maintain the reservoir pressure. The most common secondary recovery techniques are gas injection and waterflooding. 3 Tertiary recovery: We produce the hydrocarbon in this stage by injecting fluids (different from the fluids initially in the formation) into the formation through injection wells and it is called tertiary because it is applied after secondary recovery also called Enhanced oil recovery (EOR). Fig 2 shows the increase in the oil demand globally and Fig 3 shows the gap between production and consumption. Consequently, energy companies have two choices: First, they can discover new fields; the second choice is to find an advancing way to increase production for their current fields. This second option is the EOR. EOR can recover 30-60% of the original oil in place, as opposed to 20-40%, using primary or secondary recovery. Fig 4 shows that the reserves from heavy oil very huge in comparison
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History of EOR: EOR started 300 years ago. We think that the discovery of EOR was by mistake with positive results. In the beginning of 19th century, there were many oil production projects in Pennsylvania in USA. The first water flood project for oil recovery was in the 1740’s in Sweden. Although most people in the industry thought by time that water flooding was the most effective for oil recovery, other people thought that water flooding was recoverable just a part of the oil in the producing zones. However, in 1917 there was a new technology by adding alkali to the flooding water to increase its Fig 3. Egypt oil production and consumption efficiency. Now EOR methods are in proceeding and many new technologies become part of these methods.
Fig 4. Worldwide distribution of conventional crude oil and heavy hydrocarbons
Articles EOR Methods in Egyptian Fields: Recently studies have shown the most convenient EOR recovery methods for Egyptian oil fields are Miscible Flooding, immiscible gas injection, and Alkali Surfactant-Polymer injection. In addition, the cyclic steam injection, which have already been applied since 2008 in Egypt’s Issaran oil field. There is a study, shows that CO2 injection (Miscible Flooding) could help Egypt to enhance the recovery factor from 5% to 15 % and reduce the emissions of CO2 simultaneously. Fig 5 shows that the most widely used EOR project worldwide are Thermal and Miscible and these two methods could be applied in Egyptian filed. Egypt indeed has applied thermal EOR method as shown in Fig 6. Next, we will discuss Miscible Flooding and Thermal process, which could be applied in Egyptian Fields.
Fig 5. World Status of the EOR projects during 2004 –2014 from Oil and Gas Journal
since 2008. In Egypt’s Issaran oil field cyclic steam stimulation has increased production from 50 bbl/day to 4,000 bbl/day. As pointed out by Abu El-Ela, “there are 3 billion barrels of this type of heavy oil that can be recovered throughout Egypt – 40% in the Eastern Desert, 39% in the Gulf of Suez, 18% in the Sinai, and 3% in the Western Desert’’. Miscible Flooding The most popular method of EOR is Miscible Flooding. The gasses most commonly used in this process are CO2, natural gas, or nitrogen. The most gas used is CO2 because it is the cheapest one. The injected CO2 may become miscible or remain immiscible with oil, depending on reservoir pressure, temperature, and oil properties. The miscible CO2 process typically achieves higher recoveries than the immiscible process, and therefore, it is a preferred option. In Fig 8, the minimum miscibility pressure (MMP) is the pressure at which miscibility occurs
Figure 6. Status of the EOR projects in 2014 Oil and Gas Journal
Thermal Processes: They are different methods, which use thermal processes for recovering viscous oil from producing zones by heating the crude oil to reduce its viscosity and/or vaporize part of the oil and reducing its mobility ratio for easily movement to the producing well. These methods include cyclic steam injection, steam flooding and insitu combustion. Thermal Flooding began to be used in a widespread capacity in the late 1960s. Here we will discuss cyclic steam injection only which has already been applied since 2008 in Egypt and have achieved a lot of success. Cyclic Steam Injection Cyclic steam injection is also termed huff and puff; this is a stimulation method, which means that we inject and produce from the same well. In Fig 7, the first step is to inject the steam into the well for several days or weeks to heat the oil in producing formation. Then the second step the operators shut in the well several days to months to make the reservoir soaking. In this second step, the heating of oil by steam will reduce the viscosity of heavy oil and will become easy to be produced (the final step). This cyclic process of steam injection, soaking, and production can be repeated until oil recovery stops. Operators in Egypt have been using thermal flooding
Figure 7. Cyclic Steam Injection Process
that determines if the process miscible or immiscible. In Fig 9, CO2 is injected through injection well into the producing zone. This injected CO2 bank reduces the interfacial tension and dissolves in the oil, which reduce its viscosity then becomes more movable towards the production well. In Khalda concession in the north part of the Western Desert of Egypt, there is Tut field. It was figured out from studies, researches and deep investigations of Tut and surrounding locations that miscible flooding was the proper method for two reasons: the availability of CO2 in the area and the achievement of miscibility under the current reservoir conditions. In Egypt, CO2 miscible injection project has not gone into pilot test yet because of some problems associated with its implement. There is another study, showing that also Microbial Flooding is a good candidate for Egyptian fields due to oil density and viscosity, the shallow depth of the wells (2,0006,000 feet) and rock permeability.
Analysis and Discussion: The one side that all oil countries especially Egypt considered in the past, is the additional cost of implement
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Fig 8. Miscible and immiscible zones during experimental oil recovery as a function of CO2 (reservoir) pressure
Fig 9. CO2 Miscible Injection Process
these EOR methods. As well as they wanted to produce the easy oil (light and conventional oil) rather difficult oil (Heavy oil). However, there is another side that these EOR methods increase the recovery of oil from fields were considered dead (Heavy Oil). The prove on it , is shown in Fig 10 that in USA the number of CO2 projects is increasing regardless of the fluctuations of oil prices. In Egypt, the environmental impact of EOR projects also is given many considerations and it is one of the major reason for these much studies before going into applying them. The major reasons, which delay EOR techniques implementation, the need to a good infrastructure to withstand against the corrosion, the environmental considerations, the economic and political problems and definitely the fluctuations of oil prices.
Figure 10. Evolution of CO2 projects and oil prices in the U.S.
Conclusions and Recommendations: Egypt has huge reserves from heavy oil, which should be recovered to get the most benefits from them. Consequently, Egyptian oil companies should start applying EOR projects especially thermal processes and CO2 miscible injection. As we see, CO2 miscible injection is considered a promising project in Egypt because the cost of CO2 is low, its availability and it is helping the environment by getting rid of these CO2 emissions. These companies should show their willing and preparing to manage the risk of these projects. Besides, the governments have to be provide more attractive investment options for the investors. Eventually Egypt government has turned to adopt new energy systems to limit the usage non-renewable energy sources. In addition, it has the intention to increase its share from renewable energies to reach 20% by 2022. Solar and wind energy are on top of promising potentials in Egypt.
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