Gong, Yiwen (The Ohio State University) | Mehana, Mohamed (Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, USA) | Xiong, Fengyang (The Ohio State University) | Xu, Feng (Research Institute of Petroleum Exploration and Development CO., LTD, CNPC) | El-Monier, Ilham (China National Oil and Gas Exploration and Development Corporation)
Rock elastic moduli are one of the major perspectives for the hydraulic fracturing design. Among all of them, Young's modulus and Poisson's ratio essentially control fracture aperture for the proppant placement. The objective of this work is to predict the elastic moduli by applying data mining techniques as a comparison to the experimental measurements. We have collected attributes representing the pore structure, mineralogy and geomechanical characteristics. We implemented classification techniques such as k-means, hierarchical and PAM (partition around medoids). PAM results in more evenly-distributed clusters compared to the rest. Artificial Neural Network (ANN) is used for regression. We formulated two scenarios; firstly, all the data is grouped into one group and the other involves performing the regression on the clustered data. Interestingly, both scenarios yield acceptable results. The classification results could guide the fracturing operations where clusters with high brittleness, low anisotropy and high microfracture intensity could be identified as fracture candidates. Still the main limitation to unleash the machine learning capabilities in this domain is the data scarcity
This course discusses the fundamental sand control considerations involved in completing a well and introduces the various sand control techniques commonly used across the industry, including standalone screens, gravel packs, high rate water packs and frac-packs. It requires only a basic understanding of oilfield operations and is intended for drilling, completion and production personnel with some sand control experience who are looking to gain a better understanding of each technique’s advantages, limitations and application window for use in their upcoming completions.
Oil production from shale reservoirs has increased dramatically in the recent years. To identify drilling targets and optimize well completions, it is important to get early access to reservoir fluid properties. However, due to the low permeability of shale reservoirs, fluid samples often become available only after most important development decisions have been made. Therefore, it has been an abiding challenge in the industry how to acquire fluid properties data earlier in shale reservoirs.
Mud logging gas data acquired while drilling provide the earliest hydrocarbon response from the reservoir. In an earlier study, we have demonstrated that advanced mud gas data have large potential to predict reservoir fluid properties. In general, fluid properties are strongly correlated with thermal maturity of the source rock. In shale reservoirs, reservoir fluids are still in the source rock, as low permeability limits migration and convection of the reservoir fluids. As a result, the reservoir fluid systems in shale reservoirs are relatively undisturbed and have a high degree of consistency. This provides the possibility to correlate advanced mud logging gas data and reservoir fluid properties.
Based on a reservoir fluid database with more than 60 samples from different shale reservoirs, we developed a machine learning algorithm to predict fluid properties from advanced mud logging gas data. The accuracy of the new method is significantly improved compared with the previous model which used an explicit correlation based on wetness. In addition, the new approach is more general and does not depend on a specific shale reservoir. We applied the new model to 11 wells with advanced mud logging gas data. The predicted gas oil ratios are close to the measurement from early production data when advanced mud logging gas data are of good quality.
This publication demonstrates that advanced mud logging gas data can be used to acquire reservoir fluid properties in shale reservoirs. Such approach provides a novel and cost-efficient solution for the sampling challenges in early phase. In addition, the method provides continuous fluid data along entire well, as opposed to a single fluid sample taken at a specific location. Hence the results provide insight in the fluid distribution in shale reservoirs. The method can be widely used for sweet spot identification and optimizing fracking strategy in shale reservoirs.
Yang, Tao (Equinor ASA) | Arief, Ibnu Hafidz (Equinor ASA) | Niemann, Martin (Equinor ASA) | Houbiers, Marianne (Equinor ASA) | Meisingset, Knut Kristian (Equinor ASA) | Martins, Andre (Teradata) | Froelich, Laura (Teradata)
Mud gas data from drilling operations provide the very first indication of the presence of hydrocarbons in the reservoir. It has been a dream for decades in the oil industry to predict reservoir gas and oil properties from mud gas data, because it would provide knowledge of the reservoir fluid properties in an early stage, continuously for all reservoir zones, and at low costs. Previous efforts reported in the literature did not lead to a reliable method for quantitative prediction of the reservoir fluid properties from mud gas data. In this paper, we propose a novel approach based on machine learning which enables us to predict gas oil ratio (GOR) from advanced mud gas (AMG) data.
The current work is based on a previous successful pilot in unconventional (shale) reservoirs. Our aim is to extend the results of the pilot study to conventional reservoirs. In general, prediction of reservoir fluid properties is more challenging for conventional reservoirs than for unconventional reservoirs, due to the complexity of petroleum systems in conventional reservoirs. Instead of building a model directly from AMG data, we trained a machine learning model using a well-established reservoir fluid database with more than 2000 PVT samples. After thorough investigation of compositional similarity between PVT samples and AMG data, we applied the model developed from PVT samples to AMG data.
The predicted GORs from AMG data were compared with GOR measurements from corresponding PVT samples to assess the accuracy of the GOR predictions. The results from 22 wells with both AMG data and corresponding PVT samples show large agreement between prediction vs. measurement. The accuracy of the predictive model is much higher than previous results reported in the literature. In addition, a Quality Check (QC) metric was developed to efficiently flag low-quality AMG data. The QC metric is vital to give confidence level for GOR prediction based on AMG data when PVT samples are not available.
The study confirms that AMG data can be used as a new data source to quantitatively predict continuous reservoir fluid properties in the drilling phase. The method can be used to optimize wireline operations and for some cases, it provides a unique opportunity to acquire reservoir fluid data when conventional fluid sampling or use of wireline tools is not possible. After high-quality PVT data becomes available in the wireline logging phase, the continuous GOR prediction can be further improved and used to determine reservoir fluid gradient and reservoir compartmentalization.
Jie, Zhang (CNPC Engineering technology R&D company limited) | Xu, Xianguang (CNPC Engineering technology R&D company limited) | Wang, Lihui (CNPC Engineering technology R&D company limited) | Li, long (CNPC Engineering technology R&D company limited) | Zhang, Die (CNPC Engineering technology R&D company limited) | Zhao, Zhiliang (CNPC Engineering technology R&D company limited) | Wang, Shuangwei (CNPC Engineering technology R&D company limited)
Severe formation damage is induced by the invasion of working fluid and the subsequent water blocking. Surface modification by surfactant adsorption can change the wettability of the rock surface to enhance the removal efficiency of reservoir fluid and reduce the water blockage damage. Therefore, surfactant shows a good potential applicant in condense reservoir. In the current paper, an oligomeric silicone surfactant (OSSF) containing sulfonic acid groups is synthesized to improve the water flowback effect.
The critical micelle concentration (CMC) is determined by equilibrium surface tension. Micelle can be formed above the CMC and its size and distribution increase with the concentration. At the same time, the surface tension increases with the aging temperature but decreases with the adding of inorganic salt. The OSSF adsorption through solid-liquid surface can change the surface chemical composition and transfer the wettability of reservoir from water-wet to gas-wet by decreasing the surface energy. Increasing temperature leads to the change in the adsorption isotherm from Langmuir type (L-type) to "double plateau" type (LS- type). Quantum chemistry study shows that the adsorbed layer of OSSF can reduce the adhesive force of CH4 and H2O on the pore surface of cores. The OSSF can also decease the initial foaming volume and stability in induction period and accelerating period of sodium dodecyl benzene sulfonate (SDBS).
It is found that the surface tension of OSSF increases with aging temperature but decreases with the adding of inorganic salts.The OSSF has positive effect on wettability reversal to water-wet reservoir by adsorption on solid-liquid interface. The results indicate OSSF adsorption layer can change surface chemical composition and exhibit lower interface energy than that of the cores. The presence of NaCl can decrease foaming volume and improve foam stability of OSSF. At the same time, OSSF can decease the initial foaming volume and stability in induction period and accelerating period of sodium dodecyl benzene sulfonate (SDBS).
Hu, Zhenhua (PetroChina Liaohe Oilfield Company) | Zhang, Shenqin (PetroChina Qinghai Oilfield Company) | Wu, Fangfang (Schlumberger) | Liu, Xunqi (Schlumberger) | Wu, Jinlong (Schlumberger) | Li, Shenzhuan (Schlumberger) | Wang, Yuxi (Schlumberger) | Zhao, Xianran (Schlumberger) | Zhao, Haipeng (Schlumberger)
The igneous reservoir of Shahejie formation in eastern sag of Liaohe depression is characterized by complex geological environment, variable lithology and high heterogeneity. Reservoir evaluation is difficult only based on conventional logs due to complex lithology and pore structures. Effective igneous reservoirs were identified and reservoir controlling factors were analyzed based on effective porosity calculation, pore structure analysis, lithology identification, lithofacies analysis, fracture evaluation and heterogeneity analysis by combing nuclear magnetic resonance data, micro-resistivity image data, conventional logs as well as mud logging data.
Based on our study, the igneous reservoirs in the study area are more related with effective porosity and pore connectivity, and less related with fractures. Good reservoirs are mainly distributed on the top part of explosive facies and effusive facies, where lithologies are mainly Trachyte, volcanic breccia and breccia-bearing tuff. The weathering leaching process is quite important for igneous reservoirs, but the reservoir qulity would not be good if the weathering process is too strong as it will lead to low effective porosity.
The accuracy of igneous reservoir evaluation gets improved a lot by this integrated approach and the conclusion from this study will help to optimize igneous reservoire exploration plan.
Wu, Kunyu (Research Institute of Exploration & Development of Qinghai Oil Field, CNPC) | Zhang, Yongshu (Research Institute of Exploration & Development of Qinghai Oil Field, CNPC) | Zhang, Shenqin (Research Institute of Exploration & Development of Qinghai Oil Field, CNPC)
The Qaidam Basin is a big intermountain Mesozoic-Cenozoic petroliferous basin in western China. The huge thickness Cenozoic strata and tectonic deformation formed a good combination of hydrocarbon sources, reservoirs and caps, leading to huge hydrocarbon potential of the basin. The Western Yingxiongling Area locates in the western part of the Qaidam basin, during the Cenozoic era a special tectonic dynamics background was formed by the joint control of the sinistral strike-slip fault of the East Kunlun and the sinistral strike-slip fault of the Altun (
The Kenshen tight gas field, located on the northern margin of the Tarim basin, western China, has extreme reservoir conditions of an ultra_depth reservoir (6500 to 8000 m) with low porosity (2 to7%), low matrix permeability (0.001 to 0.5 md), high temperature (170 to 190°C), and high pore pressure (110-120 MPa). Those conditions result in high completion costs and a significant difference in individual well production rates; with only one-third of wells drilled meets expectations. Previous studies focused on natural fracture(NF) and attempted to classify reservoir qualities based on the density of NF. Unfortunately, some NFs were closed or cemented by clay or calcite, and it is hard to distinguish open NF from closed NFs using well images in oil-based mud, which is widely used in this tight gas field for reservoir protection. Thereby, no positive correlation between NFs density and productions has been identified, even with the same stimulation treatment.
In this study, a comprehensive geological study was conducted to find a new way of characterizing the effectiveness of NF. First, the initial and development stages of NFs were recontructed through a tectonic activity study. Two stages were detected and showed different strikes. Second, petroleum system modeling technology was applied to simulate source rock maturation and gas migration, which revealed that gas generated in the Jurassic source rock migrated to the Cretaceous reservoir formation through faults activated in the same period as the late stage of NFs development. NFs developed earlier were closed or cemented by calcite of later deposition; those at late stage were open and effective for gas charge. Also in this study, Advanced analyses of borehole images indicated an alternative way to delineate NFs developed at different stages using geometry (i.e, crossed NFs shall include those ones developed at later stage). Parallel NFs with its development unidentified can be classified through the intersection angle of fracture strike and maximum stress direction. The smaller the intersection angle is, the easier it is for stimulation and alos the higher for the well production. Based on this study, we have divided reservoirs in the study area into three classes: class 1, reservoir with crossed NFs; class 2, reservoir with fractures of small intersection angle; class 3, reservoir with fractures of large intersection angle. This innovative reservoir classification through NF geometry is currently used in the field to determine formation stimulation method. Class 1 reservoir can benefit from acidizing alone with low completion cost. Class 2 reservoir of should be hydraulically fractured with acid. Class 3 reservoir of should be fractured with sand and proppant sand to achieve economical production.
Reservoir classification with NFs geometry had been applied successfully to guide stimulation design in the Keshen tight gas reservoirs. It is a practical and feasible way to choose the most appropriate stimulation treatment method to optimize well performance and avoid restimulation to reduce costs for this extreme type of tight gas field in western China.
Is Surfactant Environmentally Safe for Offshore Use and Discharge? The current presentation date and time shown is a TENTATIVE schedule. The final/confirm presentation schedule will be notified/available in February 2019. Designing Cement Jobs for Success - Get It Right the First Time! Connected Reservoir Regions Map Created From Time-Lapse Pressure Data Shows Similarity to Other Reservoir Quality Maps in a Heterogeneous Carbonate Reservoir. X. Du, Y. Jin, X. Wu, U. of Houston; Y. Liu, X. Wu, O. Awan, J. Roth, K.C. See, N. Tognini, Shell Intl.
By International Petroleum Technology Conference (IPTC) Monday, 25 March 0900-1600 hours Instructors: Olivier Dubrule and Lukas Mosser, Imperial College London Deep Learning (DL) is already bringing game-changing applications to the petroleum industry, and this is certainly the beginning of an enduring trend. Many petroleum engineers and geoscientists are interested to know more about DL but are not sure where to start. This one-day course aims to provide this introduction. The first half of the course presents the formalism of Logistic Regression, Neural Networks and Convolutional Neural Networks and some of their applications. Much of the standard terminology used in DL applications is also presented. In the afternoon, the online environment associated with DL is discussed, from Python libraries to software repositories, including useful websites and big datasets. The last part of the course is spent discussing the most promising subsurface applications of DL.