Day 2 Wed, September 18, 2019最新文献

{"title":"Giant Field Development Optimisation with the Consideration of Regional Voidage Replacement Ratio","authors":"Najoud Hassan BaniHammad, Rachit Kedia, Jawaher Alsabeai","doi":"10.2118/196630-ms","DOIUrl":"https://doi.org/10.2118/196630-ms","url":null,"abstract":"\u0000 Net present value (NPV) and voidage replacement ratio (VRR) are the key drivers to define an optimal reservoir development strategy that maximizes returns while maintaining reservoir health. In the subsurface context, maximizing NPV consists of optimizing the well locations. Voidage replacement ratio (VRR), which is defined as the ratio between the volume of injected fluid and the volume of produced fluid, measures the rate of change in reservoir energy. Conventionally, operators try to maintain a VRR close to one during the whole field life. Typically a single value of VRR is used as a metric to represent the whole reservoir. However, this approach does not capture the lateral variation in pressure seen in giant fields.\u0000 This paper focuses on a more suitable method for determining the VRR for each user-defined pressure region using reservoir simulation. This method is used to plan the location of future wells during the long term development plan and maximize NPV and recovery. Two scenarios of well location will be examined. The first scenario consists of optimizing well location using a single VRR metric for the whole field. The second scenario uses the VRR from each pressure region to decide on the optimum number of wells per region.\u0000 This latter approach is shown to give better results in planning well location for future field development and is consistent with the reservoir pressure distribution across the field.","PeriodicalId":11098,"journal":{"name":"Day 2 Wed, September 18, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74905079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pitfalls of 3D Saturation Modelling in the Middle East","authors":"D. O'Meara","doi":"10.2118/196634-ms","DOIUrl":"https://doi.org/10.2118/196634-ms","url":null,"abstract":"\u0000 This paper shows how greater scientific rigor in discussions of modelling 3D saturations in the Middle East can lead to better understanding of the reservoirs. It demonstrates with examples how vocabulary limits ability to solve problems related to saturations, compartmentalization, and permeability. It raises the bar on technical discussions of saturation.\u0000 \"Saturation-height modelling\", \"transition zones\", and \"Thomeer hyperbolas\" are examples of terms that repeatedly confuse discussions of modelling 3D saturations in the Middle East. Vocabulary exposes a lack of scientific rigor, impedes progress, and leads to notable failures. Saturation is not merely a function of height. At the very least, it also depends on porosity, permeability, fluid densities, interfacial tension, and contact angle. Limiting it to height requires adding in all of these other functionalities as afterthoughts rather than incorporating them naturally through dimensional analysis. Most glaringly, it obscures the very useful role that saturations have in constraining permeability modelling and identifying reservoir compartments.\u0000 \"Transition zones\" focus on saturation and take emphasis away from relative permeability and fractional flow. Bimodal pore systems (abundant in the Middle East) can have such low relative permeability to water at high saturations that even 70% water saturation can produce dry oil. In such cases, talk of a transition zone is counterproductive as it implies high water production.\u0000 \"Thomeer hyperbolas\" reveal biases in how to fit capillary pressure curves. Force-fitting all data with a single model is inadequate. It takes emphasis away from understanding pore systems of rocks in favor of promoting a single-minded view. These examples and their implications are discussed in detail.\u0000 The existing literature is replete with incomplete explanations and misunderstandings that lead to notable failures in modelling Middle Eastern fields. Understandings predicated on simplified descriptions of homogeneous reservoirs are no longer sustainable. A more scientifically rigorous methodology is presented.","PeriodicalId":11098,"journal":{"name":"Day 2 Wed, September 18, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75043371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Method to Quantitatively Characterize Tight Glutenite Reservoir Pore Structure","authors":"Xuemei Dong, Ting Zhang, Weijiang Yao, Tingting Hu, Jing Li, Chunming Jia, Jian Guan","doi":"10.2118/196649-ms","DOIUrl":"https://doi.org/10.2118/196649-ms","url":null,"abstract":"\u0000 Pore structure is of great importance in tight reservoirs identification and validation evaluation, especially for formations with developed fractured. However, the conventional pore structure evaluation method based on nuclear magnetic resonance (NMR) logging lost its role. This is because the fractures with width lower than 2mm did not have response in the NMR T2 spectrum. Whereas the porosity spectrum, which extracted from the FMI data, was considered to be effective in fractured reservoir pore structure evaluation. In this study, to quantitatively characterize tight glutenite reservoir pore structure in the Jiamuhe Formation in northwest margin of Junggar Basin, northwest China, 90 core samples were drilled for lab mercury injection capillary pressure (MICP) measurement, and the XRMI data (acquired by the Halliburton and be similar with FMI) was processed to acquire the porosity spectrum. The relationship between the MICP curve and the corresponding inverse cumulative curve of porosity spectra was analyzed, and the model of piecewise power function, which can be used to transform the porosity spectrum as pseudo capillary pressure (Pc) curve, was established. By using this model, consecutive pseudoPc curves can be constructed in the intervals with which XRMI data was acquired, and the corresponding pore structure evaluation parameters, such as the average pore throat radius, the maximum pore throat radius, the threshold pressure, and so on, can also be predicted. Meanwhile, a permeability prediction model based on the Swanson parameter, also established. By combining with the constructed consecutive pseudoPc curves, the pore structure evaluation parameters and permeabilities, several hydrocarbon production potential formations were identified, and this was verified by the drill stem test (DST) data.","PeriodicalId":11098,"journal":{"name":"Day 2 Wed, September 18, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91077792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Grid and Fluid Independent Description for Multilateral Horizontal Well in Dynamic Simulation","authors":"K. Bogachev, V. Erofeev, E. Piskovskiy","doi":"10.2118/196655-ms","DOIUrl":"https://doi.org/10.2118/196655-ms","url":null,"abstract":"\u0000 The method for modeling of a multilateral well design that is completely independent on the simulation grid and fluid properties is proposed. The method takes into account friction in the lateral branches and crossflow between them. Well parameters, such as trajectory, perforation intervals, roughness and diameter, are directly used to calculate pressure distribution along the wellbore at the current fluid composition and tubing head pressure (THP).\u0000 Well connections with grid blocks in a finite volume approximation for dynamic model should be created. The automatic creation of the well connections during dynamic simulation based on specified well trajectory and completion intervals is proposed. The connection factor is suggested to be calculated based on length of completion intersection with the block, trajectory direction and rock properties during the run time. To calculate pressure drop on well track intervals between connections and the well track intervals between top completion and tubing head the well-known correlations are utilized. The correlations are used for the current fluid composition in the wellbore in each connection using information for well trajectory, roughness and diameter.\u0000 Such an approach makes it possible to get rid of the use of the tabulated bottomhole pressure (BHP) as a function of tubing head pressure for a number of phase compositions. Such traditional use of phase compositions gives a non-physical response in compositional models, where the component composition of the product varies significantly throughout the life of the field. Usage of real coordinates (x, y, z) for setting well trajectory and perforation intervals, instead of the traditional grid block numbers (i, j, k), allows to calculate layer intersection, connection factors and pressure distribution along wellbore with arbitrary changes in the dynamic model grid, for example, when introducing local grid refinement or dynamic grid and rock properties variation used to describe hydraulic fracturing.\u0000 The proposed method is successfully used for modeling of a multilateral well design in dynamic simulation. The results of such dynamic simulation are consistent with the real samples from reservoir.","PeriodicalId":11098,"journal":{"name":"Day 2 Wed, September 18, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81886550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning for 3D Image Recognition to Determine Porosity and Lithology of Heterogeneous Carbonate Rock","authors":"Omar Al-Farisi, Hongtao Zhang, Aikifa Raza, Djamel Ozzane, M. Sassi, TieJun Zhang","doi":"10.2118/196657-ms","DOIUrl":"https://doi.org/10.2118/196657-ms","url":null,"abstract":"Automated image processing algorithms can improve the quality and speed of classifying the morphology of heterogeneous carbonate rock. Several commercial products have worked to produce petrophysical properties from 2D images and with less extent from 3D images, relying on image processing and flow simulation. Images are mainly micro-computed tomography (μCT), optical images of thin-section, or magnetic resonance images (MRI). However, most of the successful work is from the homogeneous and clastic rocks. In this work, we have demonstrated a Machine Learning assisted Image Recognition (MLIR) approach to determine the porosity and lithology of heterogeneous carbonate rock by analyzing 3D images form μCT and MRI. Our research method consists of two parts: experimental and MLIR. Experimentally, we measured porosity of rock core plug with three different ways: (i) weight difference of dry and saturated rock, (ii) NMR T2 relaxation of saturated rock, and (iii) helium gas injection of rock after cleaning and drying. We performed MLIR on 3D μCT and MRI images using random forest machine-learning algorithm. Petrophysicist provided a set of training data with classes (i.e., limestone, pyrite, and pore) as expert knowledge of μCT Image intensity correspondence to petrophysical properties. MLIR performed, alone, each task for identifying different lithology types and porosity. Determined volumes have been checked and confirmed with three different experimental datasets. The measured porosity, from three experiment-based approaches, is very close. Similarly, the MLR measured porosity produced excellent results comparatively with three experimental measurements, with an accuracy of 97.1% on the training set and 94.4% on blind test prediction.","PeriodicalId":11098,"journal":{"name":"Day 2 Wed, September 18, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89008019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Data Analytics with Numerical Modeling for Optimizing Oil Field Development and Management","authors":"M. Y. Alklih, Tengku Mohd Fauzi Tengku Ab Hamid, T. Al-Shabibi, Shahab Mohagheg","doi":"10.2118/196646-ms","DOIUrl":"https://doi.org/10.2118/196646-ms","url":null,"abstract":"\u0000 Data-Driven subsurface modeling technology has been proven, for the past few years, to yield technical and commercial success in several oil fields worldwide. A data-driven model is constructed for the first time for an oil field onshore Abu Dhabi, and used for evaluation of a reservoir with substantial reserves and comprehensive development plan; for the purpose of predicting production rates, dynamic reservoir pressure and water saturation, improving reservoir understanding, supporting field development optimization and identifying optimum infill well locations. The objective is to provide the asset with a decision-support tool to make better field development planning and management.\u0000 The subject reservoir is a low permeability carbonate reservoir and characterized by lateral and vertical variations in its reservoir rocks and fluid properties. More than 8 years of Phase-I development and production/injection data and extensive amount of well tests and log data (SCAL, PVT, MDT) from more than 37 wells were used to construct the Data Driven Model for this asset.\u0000 This new modeling technology, (TDM), integrates reservoir engineering analytical techniques with Artificial Intelligence, Machine Learning & Data Mining in order to formulate an empirical and spatiotemporally calibrated full field model. In this work, it is leveraged with other conventional reservoir modeling and management tools such as streamline modeling, isobaric maps and flooding conformance.\u0000 Several analyses were performed using the full field data-driven model; complementing the existing conventional numerical model. The accomplishments of the data-driven reservoir model for this project included, but not limited to, comprehensive history matching (including blind validation) and then forecast of Oil rate, GOR, WC, reservoir pressure and water saturation, injection optimization, and choke size optimization. The results generated by the data-driven model proved to be quite eye-opening for the asset management; as the model was able to identify potential areas of improving field efficiency and cost reduction.\u0000 When combined with numerical techniques, the calibrated data-driven model assist to obtain a reliable short term forecast in a shorter time and help make quick decisions on day-to-day operational optimization aspects. The use of facts (all field measurements) instead of human biases, pre-conceived notions, and gross approximations distinguishes data-driven modeling from other existing modeling technologies. Its innovative combination of Artificial Intelligence and Machine Learning (the technologies that are transforming all industries in the 21st century) with reservoir engineering, reservoir modeling and reservoir management clearly demonstrates the potentials that these pattern recognition technologies offer to the upstream oil and gas industry for its realistic digital transformation.","PeriodicalId":11098,"journal":{"name":"Day 2 Wed, September 18, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76516572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast-Track Completion Decision Through Ensemble-Based Machine Learning","authors":"Han Xue, R. Malpani, Shivam Agrawal, T. Bukovac, A. Mahesh, T. Judd","doi":"10.2118/196702-ms","DOIUrl":"https://doi.org/10.2118/196702-ms","url":null,"abstract":"\u0000 With the advent of high-resolution methods to predict hydraulic fracture geometry and subsequent production forecasting, characterization of productive shale volume and evaluating completion design economics through science-based forward modeling becomes possible. However, operationalizing a simulation-based workflow to optimize design to keep up with the field operation schedule remains the biggest challenge owing to the slow model-to-design turnaround cycle. The objective of this project is to apply the ensemble learning-based model concept to this issue and, for the purpose of completion design, we summarize the numerical-model-centric unconventional workflow as a process that ultimately models production from a well pad (of multiple horizontal laterals) as a function of completion design parameters. After the development and validation and analysis of the surrogate model is completed, the model can be used in the predictive mode to respond to the \"what if\" questions that are raised by the reservoir/completion management team.","PeriodicalId":11098,"journal":{"name":"Day 2 Wed, September 18, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75084700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Screening of Geological Uncertainty on Reservoir Dynamic Behavior with Statistical Learning Techniques","authors":"Marco Barbiero, F. Turri, P. Anastasi, E. D. Rossa","doi":"10.2118/196712-ms","DOIUrl":"https://doi.org/10.2118/196712-ms","url":null,"abstract":"\u0000 A statistical screening methodology is presented to address uncertainty related to main geological assumptions in green field modeling. The goals are the identification of the entire range of uncertainty on production, learning which are the most impacting geological uncertain inputs and understanding the relationships between geological scenarios and classes of dynamic behavior.\u0000 The paper presents the methodology and an example application to a green field case study. The method is applied on an ensemble of reservoir models created by combining geological parameters across their range of uncertainty. The ensemble of models is then simulated with a selected development strategy and dynamic responses are grouped in classes of outcome through clustering algorithms. Ensemble responses are visualized on a multidimensional stacking plot, as a function of the geological input, and the most influential parameters are identified by axes sorting on the plot. Geological scenarios are then classified on dynamic responses through classification tree algorithms. Finally, a representative set of models is selected from the geological scenarios.\u0000 The example study application shows a final oil recovery uncertainty range of 4:1, which is reasonable for a green field in lack of data. Such high range of uncertainty could hardly be found by common risk assessment based on fixed geological assumptions, which often tend to underestimate uncertainty on forecasts. Ensemble outcomes are grouped in four classes by oil recovery, plateau strength, produced water, and breakthrough time. The adoption of such clustering features gives a broad understanding of the reservoir dynamic response. The most influential geological inputs among the examined structural and sedimentological parameters in the example application result to be the fault orientation and channel fraction. This screening result highlights the main drivers of geological uncertainty and is useful for the following scenario classification phase. Classification of the geological scenarios leads to five classes of geological parameter sets, each linked to a main class of dynamic behavior, and finally to five representative models. These five models constitute an effective sampling of the geological uncertainty space which also captures the different types of dynamic response.\u0000 This paper will contribute to widen the engineering experience on the use of machine learning for risk analysis by presenting an application on a real field case study to explore the relationship between geological uncertainty and reservoir dynamic behavior.","PeriodicalId":11098,"journal":{"name":"Day 2 Wed, September 18, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78437778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rigorous Multi-Scenario Uncertainty Analysis: An Easy Way to Create an Ensemble of Many Concepts, with Hundreds of Uncertainties, and the Power of the Cloud to Evaluate Thousands of Realizations in Hours","authors":"E. Ashoori, E. Steen","doi":"10.2118/196636-ms","DOIUrl":"https://doi.org/10.2118/196636-ms","url":null,"abstract":"\u0000 When key geological scenario uncertainties, captured in multiple conceptual models, are combined with continuous parameters, the evaluation of a representative sample set quickly becomes unmanageable, laborious and too time consuming to execute. A workflow is presented that enables users to easily model conceptual as well as parametric uncertainties of the reservoir without the necessity of any complex scripting. The chain of models for all concepts is presented in one view, to provide overview of the key differences between concepts used. An ensemble of geologically sound samples can be created taking into account parameter dependencies and probabilities of concepts. The chain of models per concept can easily be (re)executed.\u0000 A case study is presented that consists of multiple concepts based on different hierarchical stratigraphic models in combination with different fault models, each of which with its own fluid- (defined contacts per compartment), grid- (sub-layering and areal resolution) and rock property models. Volumetric calculations are run on an ensemble to get static model observables like GRV, Pore Volume, Oil-In-Place, etc., reported by multiple sub-regions of the model in combination with a lease boundary. (When coupled with dynamic simulation, observables like ultimate recovery, break-through timing, etc. could also be obtained). As thousands of realizations were run concurrently, run time was reduced from weeks to hours. Results reveal the distribution and dependency of observables like GRV on top-structure-depth uncertainty and contact-level uncertainty. For in-place volumes the full suite of concepts and other parametric uncertainties including the stochastic uncertainties (i.e. seed) is analyzed. This also enables the identification of the key uncertainties that impact equity the most, which can be of great commercial value during equity negotiations. This workflow demonstrates how, with the power of Cloud computing, rigorous evaluation of multiple concepts combined with many parametric uncertainties has been achieved within practical turn-around times. As such it overcomes the prohibitive hurdles of the past that often have led to simplifications necessary to save time and effort. The result is better decision quality in resource development decisions.","PeriodicalId":11098,"journal":{"name":"Day 2 Wed, September 18, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75739003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phased Redevelopment of a Giant Mature Offshore Field Using Maximum Reservoir Contact MRC Wells","authors":"C. Koeck, A. Bensadok, Praffula Goyal, A. Alhashmi","doi":"10.2118/196698-ms","DOIUrl":"https://doi.org/10.2118/196698-ms","url":null,"abstract":"\u0000 A giant brownfield re-development project with long horizontal wells was initiated to arrest production decline mainly caused by a lack of pressure support and free gas influx from the large gas cap.\u0000 Key value drivers for the project are developing an understanding of the layers with regards to gas breakthrough, and achieving capital efficiency through low-cost well delivery, better planning and technology applications.\u0000 Firstly, the field has been segmented based on the analysis of multiple factors influencing the free gas production. It considers geological aspects such as the study of depositional environment and diagenesis, structural elements such as high permeability streaks and fractures, dynamic behaviors such as the water injection efficiency, gas cap expansion or coning.\u0000 Secondly, numerical simulations were then run in order to rank the sectors based on the expected model performance, compare them with real data categorization, and test the effect of the new proposed development schemes such as water injection at gas-oil contact and long horizontal wells equipped with downhole control valves.\u0000 It was found that each sector has a specific production mechanism and appropriate developments were recommended and then tested in the simulation. For instance, high permeability streaks play a significant role on the development of some sectors instigating a big difference of maturity between sub-layers, early water or gas breakthrough. Also, the inefficiency of water injection is one of the biggest issues of the field. Most of the water injectors are located too far from the oil producers, and have a low injectivity due to the often degraded facies in the aquifer because of diagenesis. This leads to a lack of pressure support that is counterbalanced by the gas injection, ending up with a lot of high GOR wells and a bad sweep from the top of the structure as the gas tends to by-pass the oil.\u0000 Simulation work showed that several remaining zones are safe for immediate development and should be prioritized for development in the near future. On the other hand, some of the mature layers prone to gas and water breakthrough need a boost for development, such as water injection at gas-oil-contact, artificial lift, low pressure system, GOR relaxation. Tight and undeveloped reservoirs are improved by implementing long horizontal drains.","PeriodicalId":11098,"journal":{"name":"Day 2 Wed, September 18, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80847930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}