غزوان نوري سعد جريو

Abstract Water flooding is one of the most important methods used in enhanced production; it was a pioneer method in use, but the development of technology within the oil industry, takes this subject toward another form in the oil production and application in oil fields with all types of oils and oil reservoirs. Now days most of the injection wells directed from the vertical to re-entry of full horizontal wells in order to get full of horizontal wells advantages. This paper describes the potential benefits for using of re-entry horizontal injection wells as well as combination of re –entry horizontal injection and production wells. Al Qurainat productive sector was selected for study, which is one of the four main productive sectors of South Rumaila oil field. A simulation model – named as SRFQ was used in the present work to predict the re-entry horizontal wells performance. Four scenarios were suggested to cover the full scope of the study; those scenarios are different in manner of wells combinations. Cumulative oil production, ultimate recovery percentage are two criteria were used to predict the performance and comparison of scenarios. Results from simulation model (SRFQ) runs revealed that the productive sector can be continue to gain 1564.331 MMSTB till 2020, without changing to any existing injection and production wells status, which is called the base scenario. While scenario no.1 needs some of work over and remedies jobs, which gives more cumulative oil production reaches to 1698.481 MMSTB till 2020. On another side, scenarios no. 2 and 4 are the most important scenarios because reentry horizontal injection wells were implemented. Very good and encourage results were gained over the bas scenario from the sector under study. At last, scenario no.3 was suggested just to predict the production capacity of the Al Qurainat sector with re-entry horizontal production wells and existing vertical injection and production wells, while the cumulative oil production reaches 3398.481MMSTB. Key words: horizontal, reservoir management, increasing production, re-entry

Abstract Since the behavior of a complex dynamic system for a large oil field in Iraq is significantly influenced by many nonlinearities, its dependent parameters exhibit non-stationary with a very high delay time. Developing white-box modelling approaches for such dynamic oil well production cannot handle these large data sets with all dependent dimensions and their non-linear effects. Therefore, this study adopts the hybrid model that combines white-box and black-box to address such problems because the model outputs require various variable types to achieve optimal fitness to measured values. The hybrid model structure needs to evolve with changes in the physical parameters (white-box part) and Neural Networks' Weights (black-box part). The model structure of the proposed hybrid network relied on converting fuzzy rules in a Takagi–Sugeno–Kang Fuzzy System (TSK-FS) into a multilayer perceptron network (MLP). The hybrid parameters are formulated concerning six-dimensional dependent variables to describe them in matrix form or layer and by which can quantify total model outputs. After mapping categorical variables to tuples of MLP, the Gauss-Newton regression (GNR) provides an optimal update of the hybrid parameters to get the best fitting of the model outputs with the target of the dataset. The clustering technique and GNR promote predictive performance due to reducing uncertainties in the hybrid parameters. Due to time being the most effective of the independent variables for predicting oil production, datasets are classified into different clusters based on time. The actual field dataset for training and validation is collected from Zubair Oil Field (9 oil wells), which is implemented to build the proposed model. The results of the hybrid model indicate that the development of the proposed structure has achieved the high capability to represent such big data which is the most imperative feature of the proposed model. Furthermore, obtained results show its accuracy far outpacing competitors and achieving a significant improvement in predictive performance.

AbstractThe development of a production strategy foroil and gas fields is an important task adoptedby the directorate companies which areinvested on those fields, and the main goal oftheir plans is to increase the ultimate fieldproduction as much as possible withineconomical and physical limits.This work presents the determination of theoptimum number and location of the infillproduction new wells under developmentstage of the field life for one of the fourproduction sections of the concerning oil fieldin this study (Southern Iraqi Oil Field).A feed forward neural network approach hasbeen performed to locate the infill productionwells in order to increase the total oilproduction from that production sector.041 data sets of six variables (No. of injectionwells, No. of production wells, Injectionproduction ratio, Oil fraction, Cumulative ofinjected water and Cumulative water production )as input layer were prepared in a bundle formin order to use directly with the selected FeedForward Neural Network combining with theemployed neural network.These data sets were collected and arrangedaccording to the three concentric circles –mask calculation procedure- around theprospectus new wells in order to simplify andaccurate reservoir and production informationfor each circle calculations. One variable was gained as output layer from the constructedneural network.Good and encouraging results were obtainedthat can be serve and support the developmentphase of the concerning field case study, inaddition the results can help the directoratemanager for reaching the right decision inexecution manner. Key words – (oil field production, neural network,field management, optimization).

Abstract Horizontal good technology is one of the recent methods in the drilling and exploitation of geological formations in the best and optimal ways, because of the many and comprehensive advantages that these wells enjoy with the most important measurement and prediction index called productivity index. Which is equivalent to several times higher than the vertical well productivity index, if the field is invested by drilling vertical wells only. The current research focuses on calculating the productivity index by proposing the development point of view of the reservoir formation in the Burgan oil field through the production of oil from the production units approved in the formation (MB21, MC1, MC2) by using of horizontal good technology and comparing it with the value of the productivity index of vertical wells in the same production units. Well-known published methods adopted in the case of steady state and pseudo steady state were used in the calculations of the productivity index, and by comparison with the standard observed results, it was found that the Joshi method was closer than others in the calculations of the productivity index for both steady and pseudo steady state cases. Then the calculations were completed taking into account all conditions and factors that have a significant role in the value of the calculated productivity index. The obtained results revealed that PI increases with [well length, isotropy ratio (Kv/Kh), well thickness vs. well length] increase, while it was decreased with [ drainage area] increase in the case of steady state conditions. Also, the PI increase with length increase in the case of Pseudo steady state condition for all the studied methods, and both of Kuchuck and Economides methods seem to be close in obtained results, but Economides is the closest one to the reality and importance in application with the present case. Completion configuration has an effluence on the PI, and it is increasing with the increase of well opening to the production for such well length. Also, all the results indicated that the use of this type of technology in the field development is good and promising, and gave valuable, excellent, and optimistic results for the future production of the field in a profitable manner.

Abstract The reserve estimation process is continuous during the life of the field due to risk and inaccuracy that are considered an endemic problem thereby must be studied. Furthermore, the truth and properly defined hydrocarbon content can be identified just only at the field depletion. As a result, reserve estimation challenge is a function of time and available data. Reserve estimation can be divided into five types: analogy, volumetric, decline curve analysis, material balance and reservoir simulation, each of them differs from another to the kind of data required. The choice of the suitable and appropriate method relies on reservoir maturity, heterogeneity in the reservoir and data acquisition required. In this research, three types of reserve estimation used for the Mishrif formation / Amara oil field volumetric approach in mathematic formula (deterministic side) and Monte Carlo Simulation technique (probabilistic side), material balance equation identified by MBAL software and reservoir simulation adopted by Petrel software geological model. The results from these three methods were applied by the volumetric method in the deterministic side equal to (2.25 MMMSTB) and probabilistic side equal to (1.24, 2.22, 3.55) MMMSTB P90, P50, P10 respectively. OOIP was determined by MBAL software equal to (2.82 MMMSTB). Finally, the volume calculation of OOIP by using the petrel static model was (1.92 MMMSTB). The percentage error between material balance and the volumetric equation was equal to 20% while the percentage error between the volumetric method and petrel software was 17%. Keywords: OOIP, reserve estimation, Monte Carlo Simulation, HIIP

Abstract A well deliverability equation is important to measure production capabilities under certain conditions of reservoirs’ and bottom hole flowing pressures. Therefore, this work offered a gas deliverability equation for the Siba Gas Condensate Field in Iraq, particularly for the Yamama reservoir formation. The equation was obtained according to the methods of Houpert and Rawlins–Schellhardt for pressure-squared and pseudo pressure techniques. Results revealed that despite the lack of relevant information, the obtained deliverability equation was acceptable with mean absolute percentage errors (MAPEs) equal to 0.145053, 0.054328, 0.050463, and 0.046233, relative to available measured data. A generalized inflow performance relationship (IPR) from the literature was chosen for verification and comparison. Good and acceptable results were obtained with MAPEs equal to 0.001102726 and 0.001052612. A future IPR was derived to predict the flow behavior of the target reservoir production with a range of reservoir pressures no less than the current reservoir pressure of 8593 psia. The prediction was aimed at avoiding the loss of a significant amount of condensate in the reservoir because its measured dew point pressure is equal to 9021.35 psia. The constructed future IPR was compared with the generalized future IPR, and both showed good agreement with a MAPE equal to 5.234%.