Rumaila Oil Field Research Articles

The Impact of Drilling Operation Parameters on the Lost Circulation Problems for Oil and Gas Wells Based on Field Data

Lost circulation, the loss of drilling fluid into holes or fractures throughout the geological formations being drilled, is the most expensive problem encountered in drilling operations for oil and gas wells. It is known that when there is a loss of circulation during the drilling process, the operation activities are delayed or stopped for curing or treatment, resulting in extra time known as non-productive time (NPT). The NPT issues during the drilling operations are the most important priorities for planning any drilling program. Therefore, the lost circulation is one of the main problems which can control the NPT. To mitigate the lost circulation problems, an integrated investigation is necessarily needed for the main parameters which enhance/downgrade the rate of lost circulation.
 Drilling through the Dammam and Hartha formation is typically accompanied with substantial nonproductive time due to severe and often entire drilling fluid losses. The majority of the curing this problem require time for preparation, placement or setting. In this research, a field study was carried out to investigate the effect of 6 drilling parameters on the rate of the lost circulation. The investigated parameters are the rate of penetration, weight on bit, stand pipe pressure, mud weight, flow rate, and the equivalent circulation density. Furthermore, the effect of lithology type on the lost circulation rate was clearly determined and correlated. In addition, a graphical representation of bit depth vs the real drilling time/date was constructed to compare the lossy wells vs non-lossy wells named frithogram. This study was based on field data for 350 wells drilled in Rumaila Oilfield, southern Iraq. The results of this study showed that if these six operating parameters are standard and controlled, the significance of lost circulation problems could be reduced. However, the main factors which are responsible for the lost circulation problems are lithology types, facies and/or diagenesis process, which mainly distinguish the carbonate formations.

Estimation of Shear Wave Velocity for Shallow Depth Using Artificial Neural Network Technique: A Case Study in Rumaila oil field

In Rumaila oilfield, the lost circulation problem is a challenging issue. The geological and geomechanical properties of subsurface formations have a role in causing a circulation loss. One of the natural features related to mud loss against these formations is an unconformity surface. Inferring surface unconformity needs to be understood how the mechanical properties of rocks are distributed across the entire reservoir. The result is identifying areas of loss for solving most loss problems. Using well logs, a geomechanical model can be constructed to identify the surface unconformity. Shear wave velocity is the most crucial factor in determining mechanical properties. They are not frequently recorded during well logging for time and cost-saving purposes. save time and cost. To overcome this challenge, an ANNs model was developed to estimate the missing Vs data for the Hasa and Aruma groups in the Rumaila oil field for interested wells from the south and north domes (extending from the top of Dammam to the bottom of Hartha). The performance of the new model was tested through calibration. The outcomes showed that measured depth (MD), bulk density (RHOB), and compressional velocity (Vp) are key parameters for creating the ANN model utilizing. This study has proven that the basic systematic equations are accurately anticipate shear velocity (Vs) from conventional well logs. The correlation coefficient (R2) and the root mean square error were 0.956) and0.118, respectively.The optimum number of hidden neurons was 3 neurons). The presented model is closely resemble the measured Vs data when dataset from other well was used to check the accuracy of that predictive model. This study presents an effective, simple, and cost-effective technique, which can be used in the absence of rock tests and DTs.

Image Log Facies and Lost Circulation of the Dammam Formation in Rumaila Oil Field, Southern Iraq

One of the most useful logging tools for identifying losses and fractures is Formation Micro Imager. Drilling through the Dammam Formation is typically associated with substantial nonproductive time due to severe and often entire drilling fluid losses. The majority of remedy this problem require time for preparation, placement or setting. Combining between conventional well log and FMI log associated with drilling data, a special study of the shallow Dammam carbonate formation penetrate by wells is accomplished. For a better understanding of the loss mechanism, Image log study was highlighted to imagine the loss zones and as such adapt data to advance loss prevention or cure future scenario. Five images and conventional logs available data associated with drilling operation documents were applied to suggest best subsurface stratigraphic facies in order to knowledge sediment environment of Dammam Formation. Five image log facies were identified as representing the different environments within the different depths, large, fine vugs, carbonate layers, laminate interrupt and laminate. The stratigraphy succession reveals shallowing upward from deep water represented by laminate facies to more disturbed water signified by vuggy large facies. There are six internal picks zone according to GR log sign D90 at the top of Rus Formation, D80, D70, D65, D60, D50 and D40. It has been clear that nearly severe or complete losses events limit intensively in D60 and D65 based on logs interpretation analysis associated with drilling data.

Artificial Neural Networks to Predict Lost Circulation Zones at Southern Iraq Oilfield

Drilling soft and fragile areas such as (high permeable, cavernous, fractured, and sandy formations) have several problems. One of the most critical problems is the loss of drilling fluid into these formations in whole or part of the well. The loss of drilling fluid can lead to more significant and complex problems, such as pipe sticking, well kick, and closing the well. The drilling muds are relatively expensive, especially oil-based mud or those that contain special additives, so it is not economically beneficial to waste and lose these muds. Artificial neural networks (ANN) can predict drilling fluid losses before they occur based on drilling parameters data and drilling fluid properties of wells effected by lost circulation problems located in the same area. This paper developed two artificial neural network models to predict drilling fluid losses in the Dammam formation- Rumaila oil field in southern Iraq. The two models have the same topology and structure. The first model used the early stopping technique to stop the training when we get the global minimum and the second model used specific epochs to complete the training. The models could predict various types of losses with high accuracy. The accuracy of implementing R2 for the first and second models was 0.9302 and 0.9493, respectively. The early stopping technique lead to obtain a model with acceptable accuracy in a short time without relying on a specific number of epochs.

Lost Circulation Prediction Using Decision Tree, Random Forest, and Extra Trees Algorithms for an Iraqi Oil Field

Drilling soft and fragile areas such as high permeable, cavernous, fractured, and sandy formations are often accompanied by many problems. One of the most important of these problems is the loss of drilling fluid into these formations in whole or in part. The loss of drilling fluid can lead to bigger and more complex problems, including pipe stucking or kicking and finally closing the well. The drilling muds are relatively expensive, especially the muds that contain oil-based mud or that contain special additives, so it is not economically beneficial to waste and lose these muds. The treatment of drilling fluid losses is also somewhat expensive as a result of the wasted time that it caused, as well as the high cost of materials used in the treatment such as heavy materials, cement, and others. The best way to deal with drilling fluid losses is to prevent them. Artificial intelligence through supervised machine learning provides the possibility of predicting these losses before they occur based on equivalent circulating density, yield point, plastic viscosity, rate of penetration, flow rate, and losses rate of wells suffered from losses problem located in the same area. In this paper, three supervised machine learning models, namely, decision tree, random forest, and extra trees, were built to predict drilling fluid losses in the Rumaila oil field in southern Iraq. The results show that the extra trees model with an R2 of 0.9681was able to predict the new lost circulation events with high accuracy.

Study of Light and Heavy Minerals and Their Effect on Oil Aggregation in the Zubair Formation for the Rumaila Oil Field in Southern Iraq

Zubair Formation is the most productive reservoir in southern Iraq, which is comprised of sandstones, interbedded with shale sequences and sometimes carbonate rock. It is an important formation in the lower Cretaceous cycle in Iraq. Rumaila oil field is the largest oil field in Iraq and the 6th in the world. Two wells were studied for three depths, one in the southern Rumaila and the other in the north. The study focused on light and heavy minerals in sand fractions and their relationship with hydrocarbon assemblages. For the survey to be complete, the sedimentological study of the cores was also conducted. This research aims to determine the effect of the amount of heavy and light minerals on the generation and production of oil in the reservoir layers. The sedimentological results were the prevalence of sand for all depths, especially in Southern Rumaila. The sand ratio ranged from 62% to 98%. The rate of heavy minerals in southern Rumaila was greater than in Northern on average at 1.17%. Light minerals were the most in Northern Rumaila up to 99.3% represented in quartz with a high percentage and a little part of feldspar represented in orthoclase. Heavy minerals were represented in Opaque (24%), zircon (27.6%), tourmaline (18%), rutile (11%), biotite (3%), chlorite (3.6%), muscovite (3.6%), staurolite (2%) and hornblende (3.6%). The proportion of quartz was the most in light minerals. The average of minerals is ultra-stable, and Opaque is the most dominant over the other minerals. Ultra-stable minerals in the sand that contain hydrocarbon assemblage are an index to mineralogical maturity for sandstone rocks. The SEM images showed deformations in quartz grains due to the high energy; on the other hand, the roundness and sphericity of the grains were evident, especially in Southern Rumaila sands.

Rapid evaluation and optimization of carbon dioxide‐enhanced oil recovery using reduced‐physics proxy models

AbstractThe carbon dioxide (CO2) injection in oil reservoirs is a promising enhanced oil recovery method to reduce carbon emissions into the atmosphere. The CO2 injection simulation and optimization require a large computation time, especially in real large‐scale oil reservoirs. This paper integrates experimental design and machine learning to construct a reduced‐physics surrogate model alternative to the complex reservoir simulator. This surrogate model was used to evaluate and optimize the CO2‐assisted gravity drainage (CO2‐GAGD) process, applied to a clastic reservoir in the Rumaila oil field. In the GAGD process optimization, five operational decision parameters controlling the production and injection activities were manipulated to attain optimal future oil production. Hundreds of simulation runs were created by Latin Hypercube Sampling to build a proxy‐based optimization. The optimal scenario increased the cumulative oil production by 416 MMSTB at the end of 10‐year prediction period. Finally, four machine learning (ML) algorithms were built as proxy surrogates alternative to the complex compositional reservoir simulations: Quadratic Equation (QM), FUzzy logic‐GEnetic algorithm (FUzzy‐GEnetic), Multivariate Additive Regression Splines (MARS), and Generalized Boosted Modeling (GBM). To show how robust using the ML approaches for the fast CO2‐GAGD process optimization, the random subsampling cross‐validation was adopted to conclude the optimum proxy metamodel that provides the lowest mismatch between the proxy‐ and simulator‐based cumulative oil production. The GBM algorithm achieved the highest adjusted‐R2 (0.9973) and lowest root mean square prediction error to produce the most accurate metamodel. The resulting accurate GBM proxy metamodel can be used for fast optimization of the CO2‐GAGD process. Specifically, GBM‐metamodel should lead to achieving higher optimal reservoir flow responses, especially with running a large number of simulation runs. Consequently, this proposed trained GBM‐ML proxy metamodel could be used for gas injection optimization and uncertainty assessment with far‐less computational effort than with the conventional approaches, which use a complex flow simulator.

Facies Analysis and Depositional Environments of the Nahr Umr Formation in Rumaila Oil Field, Southern Iraq

The Nahr Umr Formation (Albian) was studied in the Rumaila oil field, southern Iraq. It consists of sandstone with high porosity and permeability, and contains a few shale overlaps that permeate the sandstone in the lower part of it, while it consists of shale and a few limestone rocks from the electrification in the upper part. The upper contact surface of this formation is conformable and graded with the Mauddud Formation. The limestone at the base of the Mauddud Formation may be on top of the black shale of the Nahr Umr Formation, and this formation is bounded from below by the Shuaiba Formation in an unconformable, which corresponds when the black shale of the Nahr Umr Formation sits on the limestone to form the Shuaiba Formation. The monocrystalline Quartz mineral is the major component of the Sandstone, with small percentages of polycrystalline quartz, feldspar, and rock fragments, according to the petrographical investigation. Mineral and textural maturity were present in the formation of rocks. Compaction, cementation, were the most important processes that influenced the rock characteristics of Nahr Umr's sandstone rocks. Six lithofacies shale, lenticular bedded sandstone–mudstone, false bedded sandstone–mudstone, siltstone–shale, cross-bedded sandstone, and parallel and cross-lamination sandstone were identified as representing the different sub environments within the different parts of transitional environments (fluvial and deltaic depositional environments), which represented oscillations in sea level rise and stillstands of the relative sea level controlled by tectonics as sediments of the basin transitioned from freshwater to marine conditions.

Evaluation of Main Pay- Zubair Formation after Operations Re-Injection of Produced Water Directly In Rumaila Oil Field Norths under Matrix Condition

The water injection program is a key part of Rumaila oilfield long-term development plan to raise pressure levels in reservoirs. This water injection program has involved project of produced water re-injection (PWRI) directly. Although PWRI is most economical method for disposal of produced water and reduces environmental pollution risks but it can causes impairment in formation permeability due to contain it on suspended and dissolved solids that can plug porous media. Therefore, it is essential conduct fluid-rocks compatibility experiments and analysis to evaluation PWRI. The experimental work was carried out in Department of Laboratory and Quality Control in Basra Oil Company, using waterflooding apparatus. The compatibility experiments were applied on five core selected from Main Pay - Zubair formation that has very high permeability with 40 liter produced water at North-Rumaila oilfield. The main purpose of this work is evaluation of PWRI by studying the reduction of permeability(formation damage). The maximum damage degree is 71% and the minimum damage degree is 55% with average value 68.2%. The main causes to impairment permeability are present high concentration from suspended solids in PW. The damage is start from maximum degree near wellbore and gradually decreasing away from injection well and the permeability start jump up. The damage zone is propagate symmetrical around axial wellbore injector and connect together formed roughly circular dish. In this paper we establish table of monitor for help to minimize formation damage. Based on these results, we can concludes, direct injection of produced water into Zubair formation without surface treatments or washing of formation or acidizing treatment or injection under fracture conditions causes formation damage and increases with time.

Determination of the Radon Concentration of Gas 222Rn Emitted From Sludge Samples Located in the Radioactive Waste Collection Warehouse in Khader Al-Maa South of Basra City – Iraq

Thirty-one samples of sludge were taken, stored at the natural radioactive waste collection site, NORM, in Khidir Al-Maa, south of Basra city, resulting from the operations of extracting and filtering crude oil in oil fields in Basra Oil Company, to determine gas concentrations Radon 222Rn and other radioisotopes in Sludge models. The rapid electronic technology was adopted through the RAD7 device, the active method, and the gamma analysis technique using HPGe High Purity Germanium Detectors to determine the specific activity of radioactive elements. The measurements for this study showed 98463±7031 Bq.m-3 in sample no. S10 a barrel of sludge was brought from the third Degassing Station plant in the northern Rumaila oil field and least concentrated is 8317±594 Bq.m-3 in sample no. S14 sludge belonging to the sixth Degassing Station / West Qurna-1, the study also revealed the presence of high concentrations of the specific effectiveness of radioactive isotopes (214Pb, 212Bi, 226Ra) and (212Pb, 212Bi, 228Ac) of the uranium-238 series and Thorium-232 series in a sludge form selected from a barrel returning to the third gas isolation plant - North Rumaila Oilfield and stockpile at the collection site, where the specific activity value of radium was 226 Ra 133851 Bq.k-1 of the uranium-238 series and for lead 212Pb 56432 Bq.k-1 of For uranium-238 series compared to their concentrations, 32 Bq.k-1 45 Bq.k-1 in a natural soil model. The effective dose to which on-site workers would be exposed in contact was calculated and found to be 196.92 Svh-1, a dose that exceeded the proposed 50 Sv. h-1 dose limits by the US EPA. The probability of developing cancer due to exposure to radon per million people was found and it was found to be a high value compared to the suggested value (170-230) per million people previously by ICRP.

Reservoir Properties of the Upper Sand Member of the Zubair Formation in North Rumaila Oil Field

The Zubair Formation is main prolific reservoirs in the Rumaila oilfield in southern Iraq. The petrophysical assessment of the Zubair Formation was carried out using a suite of open-hole logs from eight wells along with the Rumaila North oil fields, in Basra. Based on THE neutron, density and Gamma Ray logs, the Upper Sand Member of the Zubair Formation is divided into the alternate units of the sand and shale and defining their boundaries. Based on the obtained results, the Upper Sand Member was divided into three main pay units: AB, DJ, and LN, separated by two insulating shale units designated C and K. The unit DJ was subdivided into four secondary reservoir units: D, F, G, H, and the LN unit, which is split into L, M, and N. High porosity, high oil saturation and a high net-to-gross ratio were found in the petrophysical investigation. The porosity types are good (from 15 to 20%), very good (20-25%) and excellent (more than 25%) except wells R-559 and R-256, which has medium permeability, all the wells have good to excellent permeability. The net pay for the reservoir unit thickness ranges from 5 to 59m in all wells, resulting in significant hydrocarbon productivity due to strong hydrocarbon saturation, good effective porosity, and low shale volume.

Converting Carbon Dioxide in to Methane Gas and Enhance Oil Recovery by using Biotechnology Process

Using biotechnology in petroleum industry has many advantages .for example , Microbiologically Enhanced Oil Recovery (MEOR) increase of the productivity of the oil field and decrease the viscosity of the crude oil . It's known that atmosphere has considerable amount of CO2 gas as a result of industrial activities (crude oil production). CO2 gas plays a role in increasing atmosphere temperature and causing global warming. Bioremediation is a viable Biotechnology function for Re-producing depleted wells and global warming. It means Bioremediation uses metabolic adaptation of microorganism, a promising approach, using this technique employs of Methanogenic bacteria to convert CO2 gas in to CH4. Therefore turns carbon dioxide in to carbon which is added to crude oil (so contribute decrease the viscosity for heavy crude oil, This mechanism is a part of the promotion of oil production.it is apart an operation EOR. and Second reacts with Hydrogen by Bacteria to produce methane gas. The aim study, advantage this method increase production. and removal global warming. In this review, we discuss the role of Methanogenic bacteria in transforming CO2 gas into methane gas , that it has a role in crude oil production . Methanogenic bacteria have an important role in petroleum industry and environment during decreasing CO2 amount in the atmosphere and increasing reservoir pressure. MEOR technology uses strains that have a role in crude oil production; these bacterial strains produce biogases (Methane) that increase reservoir pressure. In this study, six strains were isolated from Rumaila oilfield, south of Iraq. These strains were identified based on microscopic and morphological observations. These strains were Methanogenic bacteria. The main part of this study includes identification of bacteria that can consume CO2 gas and making continual lab experiments to isolate and determine the best genus to do this process in oil field. Experiments were done in specific bio-labs for two years, Methanogenic bacteria strains were isolated by using specific selective growth media. The second part of this study is using these strains for bioremediation process of oil wells, which includes providing anaerobic conditions for these strains to transform CO2 gas to methane . Morphological and microscopic observations were conducted to these strains and showed the best kind of these strains according to the ability of transformation of CO2 to methane. The isolated bacteria were called BRS11 strain showed high efficiency in transformation of CO2 to methane.

Depositional Environment and Microfacies Analysis of Yamama Formation in North Rumaila Oil Field, South Iraq

Yamama Formation is an important sequence in southern Iraq. Petrographic analysis was used to determine and analyze the microfacies and pore types. The diagenetic processes and the impacts on the petrophysical properties of the rocks were also identified. The petrographic identification was based on data of 250 thin sections of cutting and core samples from four wells that were supplied by the Iraqi Oil Exploration Company (O.E.C). The present study focuses on the depositional environment and the microfacies analysis of Yamama Formation. The results revealed several types of microfacies, including peloidal wackestone-packstone, algal wackestone-packstone, bioclastic wackestone-packstone, foraminiferal wackestone, bioclastic wackestone, and mudstone. The latter was divided into two submicrofacies, namely argillaceous lime mudstone and sparse fossiliferous lime mudstone. The study showed that Yamama Formation was deposited through various settings within the carbonate platform; these comprise inner ramp, middle ramp, and outer ramp settings. The inner and middle ramp settings, characterized at the top and middle Formation, have high effective porosity and low clay volume, due to dominant packstone.

Spatial assessment of gross vertical reservoir heterogeneity using geostatistics and GIS-based machine-learning classifiers: A case study from the Zubair Formation, Rumaila oil field, southern Iraq

The study of oil-field reservoir heterogeneity is an important task in the oil industry as it affects waterflooding, developing injection production systems, and optimizing hydrocarbon production. In this study, vertical reservoir heterogeneity was quantified using the Lorenz statistical index, empirical Bayesian kriging, and seven machine-learning classifiers (Classification and Regression Trees, Boosted Regression Trees, Random Forest, Naïve Bayes, Logistic Regression, K-Nearest Neighbors, and Support Vector Machine with three different kernels (linear, radial, and polynomial) under the geographic information system platform. The main pay zone of the Zubair Formation in the Rumaila oil field from southern Iraq was used as a case study. The degree of heterogeneity was first quantified using the Lorenz index, and a borehole-heterogeneity inventory location map was prepared according to the determined Lorenz index. Information about five factors influencing the heterogeneity, namely, porosity, permeability, volume of shale, reservoir-unit thickness, and depth to the top of reservoir unit, was collected based on available cores, nuclear magnetic resonance log, gamma-ray logs, and drilling-information logs. Factors from these sources were interpolated to show their spatial distribution using the empirical Bayesian kriging technique. The relationship between the borehole inventory map of vertical heterogeneity and the five factors was examined using the seven machine-learning classifiers. Two statistical-error measures, namely, accuracy and Cohen's kappa, were used to verify the performance of the classifiers in both training and testing stages. Results proved that Random Forest, Support Vector Machine with radial kernel function, and Logistic Regression were the best models. The probabilities of the best performance models were then interpolated and classified into five heterogeneity zones: Very low, low, moderate, high, and very high. The high-very high classes for each of these models approximately occupy 60% of the oil field and are mainly distributed in the middle and north of the field, whereas the other classes encompass about 40% of the field and mostly occur in the south. This distribution of classes is most likely related to the distribution and complexity of former depositional environments. • The spatial vertical heterogeneity of a clastic reservoir unit is quantified in this study. • Five factors including porsoity and permeability are used as influencing factors for modeling heterogeneity. • The high zone of heterogeneity encompasses 60% of the reservoir unit.

Determination of Safe Mud Weight Window in Rumaila Oilfield, Southern Iraq

The oil and gas industry, wellbore instability plays an important role in financial losses and stops the operations while the drilling which leads to extra time known as non-productive time. In this work, a comprehensive study was carried out to realize the nature of the instability problems of the wellbore in Rumaila oilfield to improve the well design. The study goal is to develop a geomechanical model in one dimension by utilizing Schlumberger Techlog (Version 2015) software. Open hole wireline measurements were needed to develop the model. The model calibrating and validating with core laboratory tests (triaxial test), well test (Mini-frac test), repeated formation test. Mohr-Coulomb, Mogi-Coulomb, and Modified Lade are the three failure criteria which utilized to analyze the borehole breakouts and to determine the minimum mud weight needed for a stable wellbore wall. For more accuracy of the geomechanical model, the predicted profile of the borehole instability is compared with the actual failure of the borehole that is recorded by caliper log. The results of the analysis showed that the Mogi-Coulomb criteria are closer to the true well failure compared with the other two criteria and considered as the better criteria in predicting the rock failure in the Rumaila oilfield. The wellbore instability analysis revealed that the vertical and low deviated wells (less than 40º) is safer and more stable. While, the horizontal and directional wells should be drilled longitudinally to the direction of the minimum horizontal stresses at a range between 140º–150º North West-South East and the mud weight recommended is increased to 10.5 ppg to avoid most of instabilities problems. The results contribute in development plan of the wells nearby the studied area and decreasing NPT and cost.

STUDY THE RHEOLOGICAL AND OPTICAL PROPERTIES OF NICKELFERRITE /PMMA NOANO-COMPSITE GROWTH IN CRUDE OIL VIA LASER ABLATION

In this research the sample prepared in tow steps, the first step included prepared the sample to be ablated by using mixing kneading method formed as a Circular tablet contain Nickle Ferrite and Poly(methyl methacrylate)–(PMMA)polymer.Then the sample abated by using Nd:YAG laser with 1064 nm wavelength, 600 mJ energy, 6Hz frequency and 100 pulses for the tow sampleswhich immersed in dilute Crude Oil (obtained from the Rumaila Oil Field in Southern Iraq). The optical properties of samples showed thatthe same behavior but with blue shifting in S1 compared to other samples. The rheological properties showed that the viscosity increased as the polymer increased in two samples (S1 &S2) but the stability in the value of viscosity of the sample S1 that has low polymer ratio with temperature increased. The density of S1 almost double than S2 with same ablations' pulses.

Artificial Intelligence: Excellent Key for Developing E&P Oil Industry in Iraq

A Multidisciplinary study for increasing oil recovery has been made in the present paper. This work has been adopted in the Upper Sandstone member/Zubair formation in South Rumaila Oil Field. The work was achieved by using optimization techniques for determining the optimal future reservoir performance regarding to infill drilling. Two different methods of Genetic Algorithm used to optimize the number and locations of infill wells. The first method is simple adaptive genetic algorithm and the second one is the breeder adaptive. The main parameters depended in this study is the cumulative oil production obtained from the output of reservoir simulation software. These two methods of GA depend on using Net Present Value (NPV) as economic analysis as objective function. The optimal number of infill wells is three wells which have maximum cumulative oil production and maximum value of NPV. The same results from two GA methods have been obtained. The locations of these optimal infill wells located in the crest of the oil field.

Representative Sector Modeling and Waterflooding Performance in Rumaila Oilfield

Rumaila supergiant oilfield, located in Southern Iraq has a huge footprint and is considered as the second largest oilfield in the world. It contains many productive reservoirs, some known but without produced zones, and significant exploration potential. A fault divides the field into two domes to the north and south. Mishrif reservoir is the main producing reservoir in the North Rumaila oilfield. It has been producing for more than 40 years and is under depletion. However, it was subjected to water injection processes in 2015, which assisted in recovery and pressure support. Thus, requirements of managing flooding strategies and water-cut limitations are necessary in the next stages of the field life.
 In this paper, sector modeling was applied to a specific portion of the field, rather than full-field modeling, to accelerate history matching strategy and correlate static to dynamic models’ efficiently, with a minimum level of tolerance. The sector was modeled by surrounding with additional grid blocks and two pseudo wells to achieve a good matching with actual available data.
 PVT data were used for fluid modeling of a well contained in the sector, and two rock functions were inserted to the model to achieve acceptable history matching. Twelve wells were considered in this research, two of them were injectors and the remaining are producers. For future performance, some of these wells were subjected to new completion and workover processes for field development and pressure maintenance. The importance of the development plan is to represent a way for field development without new wells to be drilled. This was conducted by adding perforations to some wells, plugging the high water-cut production zones, changing production and injection rates, and converting the producers to injectors.

Physiochemical Effects of Nano Particles from Welding Fume on Rumaila Oil Field Welders by Using FESEM- EDS

Physiochemical Effects of Nano Particles from Welding Fume on Rumaila Oil Field Welders by Using FESEM- EDS

Optimization of Deviated and Horizontal Wells Trajectories and Profiles in Rumaila Oilfield

Directional and horizontal wellbore profiles and optimization of trajectory to minimizeborehole problems are considered the most important part in well planning and design. Thisstudy introduces four types of directional and horizontal wells trajectory plans for Rumailaoilfield by selecting the suitable kick off point (KOP), build section, drop section andhorizontal profile. In addition to the optimized inclination and orientation which wasselected based on Rumaila oilfield geomechanics and wellbore stability analysis so that theoptimum trajectory could be drilled with minimum wellbore instability problems. The fourrecommended types of deviated wellbore trajectories include: Type I (also called Build andHold Trajectory or L shape) which target shallow to medium reservoirs with lowinclination (20o) and less than 500m step out, Type II (S shape) that can be used topenetrate far off reservoir vertically, Type III (also called Deep Kick off wells or J shape)these wells are similar to the L shape profile except the kickoff point is at a deeper depth,and design to reach far-off targets (>500m step out) with more than 30o inclination, andfinally Type IV (horizontal) that penetrates the reservoir horizontally at 90o. The study alsorecommended the suitable drilling mud density that can control wellbore failure for the fourtypes of wellbore trajectory.