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M. F. Abu-Hashish ◽  
M. M. Abuelhassan ◽  
Gamal Elsayed

AbstractRecent advances in computer sciences have resulted in a significant improvement in reservoir modeling, which is an important stage in studying and comprehending reservoir geometry and properties. It enables the collection of various types of activities such as seismic, geological, and geophysical aspects in a single container to facilitate the characterization of reservoir continuity and homogeneity. The main goal of this work is to build a three-dimensional reservoir model of the Abu Roash G reservoir in the Hamra oil field with enough detail to represent both vertical and lateral reservoir heterogeneity at the well, multi-well, and field scales. The Late Cenomanian Abu Roash G Member is the main reservoir in the Hamra oil field. It is composed mainly of shale, carbonate and some streaks of sandstone, these streaks are shaly in some parts. Conducting the 3D geostatic model begins with the interpretation of seismic data to detect reflectors and horizons, as well as fault picking to explain the structural framework and frequently delineate the container style with proposed limitations to construct the structural model. The lithology and physical properties of Abu Roash G reservoir rock, including total and effective porosity and fluid saturation, were determined using well log data from four wells in the Hamra field. The constructed 3D geological model of the Abu Roash G has showed that the petrophysical parameters are controlled by the facies distribution and structure elements, whereas properties are the central part to the northern side of the deltaic environment than the other sides of the same environment. The model will be useful in displaying the reservoir community and indicating prospective zones for enhancing the dynamic model to improve the behavior of the flow unit productivity, as well as, section of the best sites for the future drilling.

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Ahmad Al-AbdulJabbar ◽  
Ahmed Abdulhamid Mahmoud ◽  
Salaheldin Elkatatny ◽  
Mahmoud Abughaban

This study presented an empirical correlation to estimate the drilling rate of penetration (ROP) while drilling into a sandstone formation. The equation developed in this study was based on the artificial neural networks (ANN) which was learned to assess the ROP from the drilling mechanical parameters. The ANN model was trained on 630 datapoints collected from five different wells; the suggested equation was then tested on 270 datapoints from the same training wells and then validated on three other wells. The results showed that, for the training data, the learned ANN model predicted the ROP with an AAPE of 7.5%. The extracted equation was tested on data gathered from the same training wells where it estimated the ROP with AAPE of 8.1%. The equation was then validated on three wells, and it determined the ROP with AAPEs of 9.0%, 10.7%, and 8.9% in Well-A, Well-B, and Well-D, respectively. Compared with the available empirical equations, the equation developed in this study was most accurate in estimating the ROP.

2022 ◽  
Sheng Zheng ◽  
Wei Zhou ◽  
Xiaoguang Wang ◽  
Liang Chen ◽  
Dan Xie ◽  

Abstract China has abundant low-permeability oil and gas resources. A lot of practice has proved that low-permeability reservoirs must undergo hydraulic fracturing to obtain commercial production capacity. Geomechanical characteristics are the key factor for fracturing. It plays a very important role in the oil field exploration and production. It is not only the driving force for oil and gas migration, but also provides a basis for wellbore stability analysis and drilling optimization design. The state of the formation stress field and the mechanical properties of the rock jointly determine the direction, shape and orientation of the fracture extension of the fracturing. Together it affects the stimulation effect of fracturing. Realizing the high-efficiency development of low-permeability reservoirs is a key and difficult problem facing for oil filed operator. Horizontal wells drilling and hydraulic fracturing are the core technology for increasing single well production in low-permeability reservoirs. The effectiveness of reservoir reconstruction directly determines the production capacity of the reservoir. In order to clarify the influence of static and dynamic geomechanics on the scale of reservoir stimulation in the process of horizontal well fracturing, and ultimately provide effective technical support for the formulation and optimization of reservoir stimulation design. This study is based on the study of single well one-dimensional geomechanics, using the structural characteristics and seismic attributes of low-permeability reservoirs to study the characteristics of the three-dimensional spatial distribution of mechanics. On this basis, combined with real-time fracturing treatment data and micro-seismic monitoring data, dynamic (four-dimensional) stress field simulations are continuously carried out. The research results can be mainly used to guide the optimization of reservoir stimulation and the evaluation of filed development plan.

Behzad Elahifar ◽  
Erfan Hosseini

AbstractOne of the most troublesome issues in the drilling industry is stuck drill pipes. Drilling activities will be costly and time-consuming due to stuck pipe issues. As a result, predicting a stuck pipe can be more useful. This study aims to use an artificial intelligence technology called hybrid particle swarm optimization neural network (PSO-based ANN) to predict the probability of a stuck pipe in a Middle East oil field. In this field, a total of 85 wells were investigated. Therefore, to predict this problem, we must examine and determine the role of drilling parameters by creating an appropriate model. In this case, an artificial neural network is used to solve and model the problem. In this way, by processing the parameters of wells with and without being stuck in this field, the stuck or non-stuck of drilling pipes in future wells is predicted. To create a PSO-based ANN model database, mud characteristics, geometry, hydraulic, and drilling parameters were gathered from well daily drilling reports. In addition, two databases for directional and vertical wells were established. There are two types of datasets used for each database: stuck and non-stuck. It was discovered that the PSO-based ANN model could predict the incidence of a stuck pipe with an accuracy of over 80% for both directional and vertical wells. This study divided data from several cases into four sections: 17 ½″, 12 ¼″, 8 ½″, and 6 1/8″. The key reasons for sticking and the mechanics have been thoroughly investigated for each section. The methodology presented in this paper enables the Middle East drilling industry to estimate the risk of stuck pipe occurrence during the well planning procedure.

Tadeusz Patzek ◽  
Ahmed Saad ◽  
Ahmed Hassan

Improved oil recovery from tight carbonate formations may provide the world with a major source of lower-rate power over several decades. Here we provide an overview of the Arab D formation in the largest oil field on earth, the Ghawar. We investigate the occurrence of microporosity of different origins and sizes using scanning electron microscopy (SEM) and pore casting techniques. Then, we present a robust calculation of the probability of invasion and oil saturation distribution in the nested micropores using mercury injection capillary pressure data available in the literature. We show that large portions of the micropores in Arab D formation would have been bypassed during primary drainage unless the invading crude oil ganglia were sufficiently long. Considering the asphaltenic nature of oil in the Ghawar, we expect the invaded portions of the pores to turn mixed-wet, thus becoming inaccessible to waterflooding until further measures are taken to modify the system’s chemistry.

2022 ◽  
Vol 9 ◽  
Yonghui Huang ◽  
Yuanzhi Cheng ◽  
Lu Ren ◽  
Fei Tian ◽  
Sheng Pan ◽  

Assessment of available geothermal resources in the deep oil field is important to the synergy exploitation of oil and geothermal resources. A revised volumetric approach is proposed in this work for evaluating deep geothermal potential in an active oil field by integrating a 3D geological model into a hydrothermal (HT)-coupled numerical model. Based on the analysis of the geological data and geothermal conditions, a 3D geological model is established with respect to the study area, which is discretized into grids or elements represented in the geological model. An HT-coupled numerical model was applied based on the static geological model to approximate the natural-state model of the geothermal reservoir, where the thermal distribution information can be extracted. Then the geothermal resource in each small grid element is calculated using a volumetric method, and the overall geothermal resource of the reservoirs can be obtained by making an integration over each element of the geological model. A further parametric study is carried out to investigate the influence of oil and gas saturations on the overall heat resources. The 3D geological model can provide detailed information on the reservoir volume, while the HT natural-state numerical model addressed the temperature distribution in the reservoir by taking into account complex geological structures and contrast heterogeneity. Therefore, integrating the 3D geological modeling and HT numerical model into the geothermal resource assessment improved its accuracy and helped to identify the distribution map of the available geothermal resources, which indicate optimal locations for further development and utilization of the geothermal resources. The Caofeidian new town Jidong oil field serves as an example to depict the calculation workflow. The simulation results demonstrate in the Caofeidian new town geothermal reservoir that the total amount of geothermal resources, using the proposed calculation method, is found to be 1.23e+18 J, and the total geothermal fluid volume is 8.97e+8 m3. Moreover, this approach clearly identifies the regions with the highest potential for geothermal resources. We believe this approach provides an alternative method for geothermal potential assessment in oil fields, which can be also applied globally.

SPE Journal ◽  
2022 ◽  
pp. 1-18
Marat Sagyndikov ◽  
Randall Seright ◽  
Sarkyt Kudaibergenov ◽  
Evgeni Ogay

Summary During a polymer flood, the field operator must be convinced that the large chemical investment is not compromised during polymer injection. Furthermore, injectivity associated with the viscous polymer solutions must not be reduced to where fluid throughput in the reservoir and oil production rates become uneconomic. Fractures with limited length and proper orientation have been theoretically argued to dramatically increase polymer injectivity and eliminate polymer mechanical degradation. This paper confirms these predictions through a combination of calculations, laboratory measurements, and field observations (including step-rate tests, pressure transient analysis, and analysis of fluid samples flowed back from injection wells and produced from offset production wells) associated with the Kalamkas oil field in Western Kazakhstan. A novel method was developed to collect samples of fluids that were back-produced from injection wells using the natural energy of a reservoir at the wellhead. This method included a special procedure and surface-equipment scheme to protect samples from oxidative degradation. Rheological measurements of back-produced polymer solutions revealed no polymer mechanical degradation for conditions at the Kalamkas oil field. An injection well pressure falloff test and a step-rate test confirmed that polymer injection occurred above the formation parting pressure. The open fracture area was high enough to ensure low flow velocity for the polymer solution (and consequently, the mechanical stability of the polymer). Compared to other laboratory and field procedures, this new method is quick, simple, cheap, and reliable. Tests also confirmed that contact with the formation rapidly depleted dissolved oxygen from the fluids—thereby promoting polymer chemical stability.

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