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Collaborating Authors
Abstract Oil and Gas companies are always concerned with continuously evaluating reserves of their assets. Calculating reserves is not an easy task since it requires full knowledge of many technical and non-technical aspects regarding the reservoir nature, available budget, utilized technology, economical conditions and others. Generally, the most important parameter in calculating the reserve for new fields/reservoirs is the “Recovery Factor”. Therefore, many technical approaches available to estimate hydrocarbon reserves are related to estimation of recovery factor. Recovery factor considers all the aforementioned technical and non-technical aspects. And because these aspects can be numerous, with some that can be only quantified subjectively, the first step in our approach was to standardize and normalize many of these aspects in order to formulate them into digital values to be used in Neural Network algorithms. We gathered all those aspects and classified them into two main categories: Quantitative parameters and Qualitative parameters. Quantitative parameters are normally available in digits (e.g. permeability, porosity, net-to-gross, reservoir pressure… etc.). Nevertheless, this is not the case in handling the Qualitative parameters since parameters like Technology, Asset Remoteness and reservoir structure complexity are not normally represented in digits. By using wide range of actual fields' data, we had a very good calibration and control points on the algorithm we are going to use. These data include almost all the governing parameters such as Asset Economics, Technology, Facilities, Start of Production, Number of Wells, Reservoir Architecture, Rock and Fluid Properties, Reservoir Energy and others. Since some of these aspects may be hard to find, we generated two well-trained Artificial Neural Network (ANNs): (1) Simple NN includes a few “easy-to-know” basic data, and (2) Sophisticated NN that includes many advanced data with reasonable accuracy. These NNs were taught with more than 150 lessons at which the inputs (lessons) were the aforementioned “parameters” and the output was the “Recovery Factor”. Testing both the simple and sophisticated networks showed prediction capabilities of 9.5% and 8.0% of actual recovery factor, respectively.
- Geophysics > Seismic Surveying (0.47)
- Geophysics > Borehole Geophysics (0.46)
Abstract GUPCO is one of the largest E&P Companies in Egypt and Middle East. It has a vast infrastructure with a large number of wells, platforms, pipelines and offshore facilities. GUPCO's peak production exceeded 600,000 BOPD in 1983 while it produces around 100,000 BOEPD today from more than ten geological formations in Gulf of Suez (GoS). GUPCO produced more than 4.6 billion STBO which represent more than 43% of Egypt's total cumulative oil to date. And in spite of that, we still have many opportunities and success yet to achieve. As one of the petroleum industry leaders, GUPCO was and will always seek success and excellence in managing its assets. For more than fifty years, GUPCO used to follow the highest standards available in petroleum industry, and applied them in all areas to achieve that outstanding excellence. From day one, GUPCO realized that understanding subsurface features and optimizing recovery from different fields are the key areas among all. As a result, GUPCO had made concerted efforts in those areas in specific. Managing giant fields is not an easy task; it requires special knowledge and experience to manage such critical asset since each 1% increment of oil recovery means tens of millions of oil barrels. And because GUPCO has four giant fields, it was serious for us to do our best to maximize their value. GUPCO started that early whilst exploration phase, appraisal, development and currently in maturity phase. Along these different phases, we utilized wide spectrum of tools starting from basic technical elements (e.g. flow equations, DCA, MBE, PTA… etc.) reaching to state-of-the-art techniques and technology available (e.g. Numerical Modeling, Artificial Intelligence and EOR) at which GUPCO uses numerical reservoir simulation extensively, utilizes neural network in different applications, and already applied TAP (Thermally-Activated Particles) technique which is called commercially BrightWater to improve sweeping efficiency, and also studied feasibility of Low Salinity Waterflooding (LoSal) which is planned to be implemented in near future after upgrading water injection facilities. In this paper, we are going to present various case studies with detailed elements GUPCO followed to accomplish that success in managing giant fields, and how such powerful techniques contributed in maximizing our asset value. We'll explain also what we did from day one highlighting different challenges we faced and how we managed to solve them, and more importantly, we'll elaborate our experience in dealing with giant fields under all levels of field's maturity, highlighting the importance of many tools we've utilized. And ultimately, proposing guidelines to be followed whilst applying Waterflooding for utmost benefit.
- Geology > Geological Subdiscipline (0.68)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.46)
- Energy > Oil & Gas > Upstream (1.00)
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (0.36)
- Africa > Middle East > Egypt > Gulf of Suez > Gulf of Suez Basin > Ramadan Field (0.99)
- Africa > Middle East > Egypt > Gulf of Suez > Gulf of Suez Basin > October Field (0.99)
- Africa > Middle East > Egypt > Gulf of Suez > Gulf of Suez Basin > Nubia Formation (0.99)
- (8 more...)
Abstract Oil and gas operating companies are always concerned with evaluating the reserve of their assets. Evaluation process of hydrocarbon reserves requires a full understanding and knowledge of technical and non-technical aspects regarding the nature of reservoir, available technology and economic conditions as well as others. Recovery factor (RF) is the most important parameter in evaluating the reserve of new fields. Several techniques are currently available for estimating oil recovery factor, the accuracy of those techniques are highly affected by data availability which is mainly related to the field age. Some of the techniques are highly accurate but they require lots of production data, hence, their applicability early in the reservoir life is restricted. Others could be applied earlier, but on the other hand, they have very low accuracy. In this paper ten parameters (original oil in place, asset area, net pay, initial reservoir pressure, porosity, permeability, Lorenz coefficient, API gravity, initial water saturation and oil viscosity), which are usually available early in the life of the reservoir, are used to estimate the oil recovery factor through application of four Artificial Intelligence (AI) techniques namely: artificial neuron networks (ANNs), Radial Basis Neuron Network (RNN), ANFIS-2 (Adaptive Neuro Fuzzy Inference System, Subtractive Clustering), and SVM (Support Vector Machines). Data from 130 sandstone reservoirs were used to learn the AI models, and then an empirical correlation was developed based on the ANN model. The suggested AI models and the developed ANN-based correlation were then tested in other 38 sandstone reservoirs. The results obtained showed that ANN-based correlation successfully predicted the recovery factor based on early time data only with absolute average percentage error (AAPE) of 7.92%, coefficient of determination (R) of 0.9417, root mean square error (RMSE) and maximum absolute percent error (MAE) of 3.74 and 24.07%, respectively. ANN-based empirical correlation over-performed RNN, ANFIS-2, and SVM models in term of AAPE, MAE, and RMSE for testing set. Comparison of the recovery factor predicted by the developed equation with three available correlations showed that the developed equation predictability is about 5 times better that the most accurate correlation (of the currently available ones) in term of AAPE for predicted RF of the tested 38 reservoirs.
- Asia > Middle East > Qatar (0.28)
- North America > United States > California > San Francisco County > San Francisco (0.15)
- Asia > Middle East > Qatar > Arabian Gulf > Rub' al Khali Basin > Al Shaheen Field > Shuaiba Formation (0.99)
- Asia > Middle East > Qatar > Arabian Gulf > Rub' al Khali Basin > Al Shaheen Field > Nahr Umr Formation (0.99)
- Asia > Middle East > Qatar > Arabian Gulf > Rub' al Khali Basin > Al Shaheen Field > Mauddud Formation (0.99)
- (8 more...)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Support Vector Machines (0.89)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Fuzzy Logic (0.88)
Abstract The lifecycle of any field consists of three main periods; green, plateau and maturity periods. Currently most of GUPCO fields are brown what made us very concerned to sustain and even increase our production. To achieve that, we have looked at new different options to exploit our resources better. Generally, this can be achieved by whether optimizing current system, applying new technology or evaluating unconventional resources. One of the high-potential resources that we do have in GUPCO is unconventional resources with many tight carbonate formations. Nevertheless, we did not try to appraise it before since most of our reservoirs are clastics that can be easily characterized and evaluated. On the other hand, tight carbonate formations cannot be characterized or appraised utilizing conventional logging tools or even classical reservoir engineering concepts. It always requires unique techniques relevant to its unique complexity degree especially in presence of micro-porosity and unknown fluid content. This paper sheds light on Appraisal Unconventional Resource Study that resulted in the first successful producer in the company. GUPCO started to appraise tight carbonate rocks (named Thebes in Lower Eocene) and basaltic intrusion in GoS. This study involved high integration between key disciplines; Petrophysics, Petrology and Reservoir Engineering. To manage uncertainty, we have acquired wide range of data types starting from advanced petrophysical logging tools like Magnetic Resonance, Borehole Imaging and spectroscopy, and full petro-graphic description, reaching to predicting reservoir dynamic performance using measured pressure points (RFT), its analysis and fluid characterization. Ultimately, we have succeeded to completely characterize Thebes formation, and proposing its development plan. The first successful well resulted in 300 BOPD gain as the first successful tight carbonate producer in GUPCO. Development plan is being built to drill new wells targeting unconventional resources including a few possible potential in basalt intrusions, as well. Dealing with unconventional resources is not an easy task. It requires a lot of work and analysis. Having all of your homework done is not always enough, you have to integrate with interrelated disciplines to link dots and complete the picture. In this paper, we have conceived a new approach in evaluating such formations, and it is a very good example of managing uncertainty by integrating different data to convert hypothesis into reality that can be translated ultimately into oil production and revenues.
- Asia > Middle East > Saudi Arabia (1.00)
- Africa > Middle East > Egypt > South Sinai Governorate (0.24)
- Africa > Middle East > Egypt > Gulf of Suez (0.15)
- Geology > Rock Type (1.00)
- Geology > Petroleum Play Type > Unconventional Play > Heavy Oil Play (1.00)
- Geology > Geological Subdiscipline (1.00)
- South America > Argentina > Mendoza > Cuyo Basin > Vizcacheras Field (0.99)
- North America > United States > Texas > October Field (0.99)
- Africa > Middle East > Egypt > South Sinai Governorate > Lagia Field > Thebes Formation (0.99)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Oil sand, oil shale, bitumen (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Thermal methods (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Microbial methods (1.00)
- (2 more...)
Abstract Since easy oil is diminishing, the entire world started to look after unconventional resources to meet international demand of hydrocarbons. Heavy oil resources represent more than 40% of natural resources within our Earth crust; that is why it has a special focus internationally. Gulf of Suez Petroleum Company (GUPCO) is a leading E&P company that produced c. 40% of Egypt's total cumulative oil to date. In spite of that success, GUPCO recovered only c. 2.0 MMSTBO out of c. 500+ MMSTBO of heavy oil resources in Gulf of Suez. Historically, many efforts were spent in order to exploit such huge volume but in vain due to the reservoir harsh-conditions at which most of conventional approaches fail. This paper addresses successful application of Microbial EOR as Huff'n Puff Pilot to recover heavy oil in Gulf of Suez, Egypt. GUPCO heavy oil resources is mainly in Nubia formation, October field (offshore). Nubia is a thick fluvial system high quality deep reservoir (11,250 feet tvdSS) with 290 deg. F. temperature, 190,000 PPM Salinity, 14 deg. API, 600 cP viscosity, 2800 psi average reservoir pressure with inter-bedding tar mats. Such conditions (especially temperature, salinity and depth) are barriers for conventional EOR methods especially in offshore environment, and here it comes the special role of reservoir indigenous microorganisms. Indigenous bacteria can be induced by organic fertilizers to produce biological metabolites that may help in recovering heavy oil. The MEOR Pilot was studied in three phases; Phase-1: Sampling & Microbiological Assessment, Phase-2: Lab Experiments & Fermentation and Phase-3: Execution. A water sample was taken from the heavy oil well, and microbiological assessment concluded presence of five species of indigenous bacteria that can produce biogases, bio-surfactant and biopolymers. Second phase involved fertilizers optimization by addressing optimum P/N ratio, organic/inorganic sources and growth rate in addition to fermentation process in the lab. Several core-flooding tests were conducted to define the prize at core-plug scale. After two years of research, the optimum fertilizer was fermented that resulted in 12% heavy oil recovery for one PV Injected. Third phase was pilot implementation at which 160 barrels of fertilizers were injected into the well. The well was shut-in for soaking for 3 days, and then re-opened. After treatment, the oil API increased from 24 deg. up to 40 deg., oil viscosity from 630 cP down to 390 cP, Asphaltenes content from 14.5% to 9.6% with 412 BOPD gain. By modeling these results, MEOR succeeded to achieve a recovery of 41% of the drainage radius STOOIP that is very promising in flooding applications which is being planned at time being. MEOR was found very efficient in recovering heavy oil in a technically challenging reservoir. Lab experiments and fertilizers optimization are key stages that have many know-hows. In this paper, laboratory experiments will be elaborated, pilot design and execution operational considerations will be presented as well. This paper is a good example of a typical MEOR study workflow that can be followed by E&P companies need to unlock such challenging resources in their portfolio.
- Africa > Middle East > Egypt > Gulf of Suez (0.69)
- Africa > Middle East > Egypt > Suez Governorate > Suez (0.24)
- South America > Argentina > Mendoza > Cuyo Basin > Vizcacheras Field (0.99)
- Africa > Middle East > Egypt > Gulf of Suez > Gulf of Suez Basin > October Field (0.99)
- Africa > Middle East > Egypt > Gulf of Suez > Gulf of Suez Basin > Nubia Formation (0.99)