|Theme||Visible||Selectable||Appearance||Zoom Range (now: 0)|
SPE, through its Energy4me programme, will present a free one-day energy education workshop for science teachers (grades 8–12). A variety of free instructional materials will be available to take back to the classroom. Educators will receive comprehensive, objective information about the scientific concepts of energy and its importance while discovering the world of oil and natural gas exploration and production. Energy4me is an energy educational public outreach programme that highlights how energy works in our everyday lives and promote information about career opportunities in petroleum engineering and the upstream professions. SPE’s Energy4me programme values the role teachers and energy professionals play in educating young people about the importance of energy.
Facies classification is significant for characterization and evaluation of a reservoir because the distribution of facies has an important impact on reservoir modelling which is important for decision making and maximizing return. Facies classification using data from sources such as wells and outcrop cannot capture all reservoir characterization in the inter-well region and therefore as an alternative approach, seismic facies classification schemes have to be applied to reduce the uncertainties in the reservoir model. In this research, a machine learning neural network was introduced to predict the lithology required for building a full field earth model for carbonate reservoirs in Sothern Iraq.
In the present research, multilayer feed forward network (MLFN) and probabilistic neural network (PNN) were undertaken to classify facies and its distribution. The well log that was used for litho-facies classification is based on a porosity log. The spatial distribution of litho-facies was validated carefully using core data. Once successfully trained, final results show that PNN technique classified the carbonate reservoir into four facies, while the MLFN presented two facies. The final results on a blind well, show that PNN technique has the best performance on facies classification. These observations implied this reservoir consists of a wide range of lithology and porotype fluctuations due to the impact of depositional environment.
The work and the methodology provide a significant improvement of the facies classification and revealed the capability of probabilistic neural network technique when tested against the neural network. Therefore, it proved to be very successful as developed for facies classification in carbonate rock types in the Middle East and similar heterogeneous carbonate reservoirs.
Integrating discrete facies classification into the estimation of formation permeability is a crucial step to improve reservoir characterization and to preserve heterogeneity quantification. Therefore, it is essential to obtain the most accurate estimation of permeability in non-cored intervals in order to attain realistic reservoir characterization and modeling. In our most recent paper [OTC-30906-MS], the electrofacies classifications have been conduced for a well from a carbonate reservoir in a Giant Southern Iraqi oil field. The same predicted discrete electrofacies distribution was included in this paper along with well logging interpretations to model and predict the reservoir core permeability for all wells. The well logging interpretations that were included in permeability modelling are neutron porosity, shale volume, and water saturation as a function of depth. The regression and machine learning approaches adopted for permeability modelling are multiple linear regression (MLR), smooth generalized additive Modeling (SGAM) and Random Forest (RF) Algorithm. The classified electrofacies were considered as a discrete independent variable in the core permeability modelling to provide different model fits given each electrofacies type in order to capture the different permeability variances.
The matching visualization between the observed and predicted core permeability, the computed root mean square prediction error and adjusted squared R were considered as validation and accuracy tools to compare between the three modelling approaches. Since there are too many intervals with missing core permeability measurements, the modelling was first adopted on the intervals that have permeability readings (known subset). The prediction was then conducted given the same known permeability intervals in addition to the entire dataset (full dataset). The root mean square prediction error and adjusted squared R for the Random Forest were significantly better than in both MLR and SGAM for the known subset and full dataset. It can be concluded that combining the electrofacies in one permeability model has accurate, fast and an automation procedure of prediction for other wells. The two machine learning algorithms were implemented by R software, the most powerful statistical programming language.
Understanding the vertical discrete electrofacies distributions in wells is a vital step to preserve the reservoir heterogeneity. Predicting the electrofacies distribution at all wells is commonly conducted manually or with the use of some graphing approaches, but recently different machine learning techniques have been adopted to categorize electrofacies. In this paper, two supervised machine-learning techniques were implemented to model electrofacies given well logging data for a well in order to predict the distributions in all other wells (classification) in a carbonate reservoir in a giant southern Iraqi Oil Field.
The available data included open-hole and CPI well logging records in addition to the routine core analysis. The well discrete electrofacies distribution for the entire reservoir thickness has been obtained in our paper [OTC-29269-MS] using the Ward Hierarchical Clustering Analysis. For electrofacies classification, two supervised machine-learning techniques, K-Nearest Neighbors (KNN) and Random Forests (RF), were adopted to model the resulting electrofacies given the CPI well logging data for a well to predict at other wells that have missing data. These two supervised learning techniques were implemented as non-linear and non-parametric classifiers, which are imperative attribute due to the non-linearity of the electrofacies properties and the geological reservoir control.
The results of this research illustrated that the reservoir electrofacies can be predicted through the use of the supervised learning techniques when well logging records and core data are available. The two adopted classification algorithms were analyzed and compared based on confusion table, transition probability matrix and total percent correct (TCP) of the identified electrofacies that reveal the accuracy of the classification. RF was observed to be the optimum approach as it led to better electrofacies classification in this carbonate reservoir than the KNN.
The application of supervised machine learning techniques enhanced the accuracy and reduced the time spent in electrofacies classification. The two machine learning algorithms were implemented by R software, the most powerful statistical programming language.
Summary We propose a new three-step methodology to perform an automated mineralogical inversion from wellbore logs. The approach is derived from a Bayesian linear-regression model with no prior knowledge of the mineral composition of the rock. The first step makes use of approximate Bayesian computation (ABC) for each depth sample to evaluate all the possible mineral proportions that are consistent with the measured log responses. The second step gathers these candidates for a given stratum and computes through a density-based clustering algorithm the most probable mineralogical compositions. Finally, for each stratum and for the most probable combinations, a mineralogical inversion is performed with an associated confidence estimate. The advantage of this approach is to explore all possible mineralogy hypotheses that match the wellbore data. This pipeline is tested on both synthetic and real data sets. Introduction One of the main goals of reservoir evaluation is the determination of petrophysical parameters such as porosity, permeability, or water saturation. To obtain an accurate estimate of these parameters, a complete characterization of the lithology or the nature of the rocks is necessary. The petrophysicist proceeds to the analysis of wellbore logs, which often requires input from an expert. Indeed, petrophysical inversion of wellbore logs yields a selection of minerals or fluids belonging to the formation usually with more unknowns (the mineralogy) than measurements (the logs). In a bulk-density/neutron-porosity crossplot, an expert can identify the presence of gas, limestone, or an exotic mineral.
Abbas, Ahmed K. (Iraqi Drilling Company, Missouri University of Science and Technology) | Alhameedi, Hayder A. (University of Al-Qadisiyah, Missouri University of Science and Technology) | Alsaba, Mortadha (Australian College of Kuwait) | Al Dushaishi, Mohammed F. (Oklahoma State University) | Flori, Ralph (Missouri University of Science and Technology)
Coiled tubing (CT) technology has been widely used in oilfield operations, including workover applications. This technology has achieved considerable economic benefits; however, it also raises new challenges. One of the main challenges that were encountered while using this technology is the buckling of the CT string. It can occur when the axial compressive load acting on the CT string exceeds the critical buckling loads, especially in highly deviated/horizontal and extended reach wells. Moreover, this issue becomes more critical when using non-Newtonian fluids. Therefore, the major focus of this study is to identify the frictional pressure loss of non-Newtonian fluids in an annulus with a buckled inner tubing string.
In the present study, a laboratory-scale flow loop was used to investigate the influence of various buckling configurations (i.e., sinusoidal, transitional, and helically) of the inner pipe on the annular frictional pressure losses while circulating non-Newtonian drilling fluids. The experiments were conducted on a horizontal well setup with a non-rotating buckled inner pipe string, considering the impact of steady-state isothermal of laminar, transition, and turbulent flow regions on frictional pressure losses. Six different Herschel-Bulkley fluids were utilized to examine the dependence of pressure losses on fluid rheological properties (i.e., yield stress, consistency index, and flow behavior index).
Experiments showed potential to significantly decrease the frictional pressure losses as the axial compressive load acting on the inner pipe increases. The effect of buckling was more pronounced when fluids with higher yield stress and higher shear-thinning ability were used. In addition, by comparing the non-compressed and the compressed inner pipe, an additional reduction in frictional pressure losses occurred as the axial compressive load increased. However, the effect of the compressed inner pipe was insignificant for fluids with a low yield stress, consistency index, and high-flow-behavior index, especially in the laminar region. The information obtained from this study will contribute toward providing a more comprehensive and meaningful interpretation of fluid flow in the vicinity of a buckled coiled tubing string. In the same manner, accurate knowledge of the predicted friction pressure will improve safety and enhance the optimization of coiled tubing operations.
Cossa, Alessandro (Eni S.p.A) | Guglielmelli, Andrea (Eni S.p.A) | Rotelli, Fabiana (Eni S.p.A) | Bazzana, Michele (Eni S.p.A) | Callegaro, Chiara (Eni S.p.A) | Raimondi Cominesi, Nicola (Eni S.p.A) | Bigoni, Francesco (Eni S.p.A) | Pirrone, Marco (Eni S.p.A) | Ali Hassan, Al Attwi Maher (ZFOD) | Ibrahim Uatouf, Kubbah Salma (ZFOD)
Carbonate reservoirs are often characterized by karst features occurrence, usually related to a significant permeability enhancement in presence of low porosity and low permeability matrix type sediments. The distribution of such karst features is generally highly heterogeneous and difficult to predict, making the reservoir management challenging.
In Zubair Field (Iraq), there are numerous evidences of karst events within the Upper interval of Mishrif Formation. The production behavior of Upper Mishrif is therefore very heterogeneous, moving from wells with relatively low flow capacity, as expected from petrophysical interpretation, to wells with a very high flow capacity, hence related to karst enhanced permeability. The integration of petrophysical interpretation, well test and multi-rate production logging allowed to preliminary highlight the improved permeability intervals associated to karst. In addition, accurate image log analysis on the same wells investigated a possible relationship between vug densities and production data, to be extended also to wells lacking the latter data. This process allowed to define a karst flag in more than 60 wells.
Then, correlations between karst features and different seismic and geological attributes were identified. The most meaningful parameters were used as input data for a Neural Net Process, leading to the definition of a probability 3D Volume of karst occurrence.
The final outcomes of the workflow are karst probability maps, used as a driver for the definition of new wells targets and associated trajectories. The recent drilled wells, with optimized paths according to these prediction-maps, have demonstrated the reliability of this approach intercepting the desired karst intervals. This study represents a valuable opportunity in terms of understanding of the reservoir behavior and impact on the ongoing intensive drilling campaign and related field performance.
Copyright 2020, International Petroleum Technology Conference This paper was prepared for presentation at the International Petroleum Technology Conference held in Dhahran, Saudi Arabia, 13 - 15 January 2020. This paper was selected for presentation by an IPTC Programme Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the International Petroleum Technology Conference and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the International Petroleum Technology Conference, its officers, or members. Papers presented at IPTC are subject to publication review by Sponsor Society Committees of IPTC. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the International Petroleum Technology Conference is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of where and by whom the paper was presented.
Electrofacies identification is a crucial procedure in reservoir characterization especially in the lack of lithofacies measurements from core analysis. Electrofacies classification is essential to improve permeability-porosity relationships in non-cored intervals. Flow Zone Indicator (FZI) is a conventional procedure for rock types classification whereas Clustering Analysis has been recently used as unsupervised machine learning technique to group a set of data objects into clusters with no predefined classes. In this paper, clustering analysis and flow zone indicator were adopted for the electrofacies characterization on a dataset obtained from incorporate of conventional core analysis and CPI logs (Effective Porosity, Water saturation and Shale volume) of three wells in the upper shale member/Zubair formation in Luhais oil field southern Iraq.
Dahm, Haider H. (University of Misan) | Abbas, Ahmed K. (Iraqi Drilling Company) | Alhumairi, Mohammed A. (University of Misan) | Alsaba, Mortadha (Australian College of Kuwait) | Mohammed, Haider Q. (Basrah Oil Company) | Al-Hamad, Nasser (Schlumberger)
Drilling operations in deep shales are more challenging due to geomechanical problems such as wellbore instability. Neglecting the impact of geomechanical issues may lead to drilling drawbacks such as loss of circulation, wellbore collapse, tight hole while tripping, stuck logging tools, and subsequent fishing, stuck pipe, and sidetracking. The directional dependency (anisotropy) of the rock properties, especially the rock strength, cause variation in the wave velocities. Identifying orientation and degree of the anisotropy and its relationship with geomechanical problems is essential for further field development. In this paper, acoustic data from vertical and deviated wells in Zubair Formation, Southern Iraq were performed to provide insights on the state of stress distribution around the wellbore through the Zubair Formation. In addition, interpreting the flexural dispersion curves as the final result of cross-dipole data processing, the maximum stress direction has been obtained to illustrate the type and source of anisotropy around the borehole in this formation. Using the flexural dispersion curves and the shear wave splitting approaches, the direction and the degree of anisotropy were determined in Zubair formation. Results indicated that drilling operations have altered the state of stress around the wellbore, and the degree of alteration is a function of the magnitude and the direction of the anisotropy. The flexural dispersion curve shows that the upper member of Zubair Formation is strongly anisotropic, whereas a slight anisotropy was mapped within the middle member of the same formation. In addition, shear wave splitting analysis revealed that the stress changes along the direction striking from north to west. Thus, considerable attention should be paid to all 1D-geomechanicsl models that constructed for Zubair Formation given its strong aviation of the magnitude and direction of its strength and elastic properties.