bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Innovative Reservoir Engineering Method to Identify Bypassed Oil and Derisk the Further Development of a Complex Mature Field in Offshore, Malaysia
Authors: Mandal Dipak , Musani Nazim , Mohmad Nis Ilyani 
Published: April 2019

Abstract
In today's fast paced and challenging oil industry, the need of faster evaluation studies for quick generation of field development plan (FDP) is becoming more crucial to remain competitive. Field's geological and structural complexity, uncertainty of production data adds to the challenges. Traditional approach of building dynamic mesh models carrying out numerical simulation to history match, then predict has always remained time consuming in large mature fields.
The ‘B’ field in Peninsular Malaysia is a mature clastic with stacked reservoirs having a huge gas cap with moderate aquifer. Significant production over last 30+ years led to uneven movement of the gas cap and also of the edge aquifer leading to possibility of bypassed oil. The updated dynamic model could not match the preferential gas cap movement, thus failed to match the high GOR of downdip wells and also unable to match high watercut of certain updip wells. To identify the areas of bypassed oil thus is a significant challenge with the current dynamic model. New engineering tools of polygon balancing, material balance, normalized EUR bubbles were used with the 3D static model volume and the facies understanding. The uncertainties and risks were also identified and clear measurable methods were proposed to address the uncertainties and reduce the risks. Very detailed decision tree with clear data gathering plan to drill successive optimum wells have been planned during the campaign.
This paper details the new engineering tools used to delineate and quantify the bypassed oil in these huge clastic reservoir with preferential gas and water movement, unable to be history matched by the dynamic model. It explains the engineering methods applied to identify and quantify the 10 infill wells proposed for the development campaign. To reduce risks, this paper would also explain the blind testing that was carried out on for this new reservoir engineering analysis tool by deriving the infill potentials of the previous campaign (4 years back) by the same method.
The paper details how robust technical development plans were generated having infill well locations and reserve determination. This paper will also demonstrate the classic "Do-Learn-Adapt" strategy through its infill wells prioritization & ranking, subsurface de-risking analysis, data acquisition and mitigations plans.
Forward Integration of Dynamic Data into 3-D Static Modeling Significantly Improves Reservoir Characterization
Authors: Kayode Babatope , Meza Otto , Quintero Nerio , AlDossary Shaikha 
Published: April 2019

Abstract
Geo-modelling is usually done to honor static data such as core, well logs and seismic acoustic impedance (AI) map where available. Once the static geo-model is complete, history matching is carried out by tuning the static model properties until the model reproduces observed dynamic behavior. The objective of this paper is to showcase how a systematic a priori integration of dynamic elements into geo-modelling eliminated the need for history matching. These dynamic elements are; connected reservoir regions CRR (Kayode et.al 2018) and permeability-thickness (kh) interpretation from Pressure Transient Analysis PTA. This paper also introduces the concept of CRR based permeability modeling.
CRRs were defined based on time-lapse shut-in pressure trend groups. Core and log data were grouped on the basis of the identified CRR and used to build CRR-based Neural Network models for predicting permeability logs of non-cored wells within each CRR. The geo-modeler then created two geo-realizations by using the permeability logs within each CRR to distribute permeability within the CRR using two assumptions of variogram lengths (i) variogram range obtained from analysis of limited core data, (ii) variogram range required to ensure intra-CRR connectivity. Pressure transient was simulated for wells with observed PTA data using the two realizations, and a comparison of the log-log plots of simulated pressure transient derivative and observed pressure transient derivative were used to determine the quality of each realization for each well. The realization that provided the least squares of error across all the wells was selected as base-case geo-model. Permeability correction coefficients were applied on the base-case geo-model until PTA kh were acceptably matched. The resulting permeability log at the PTA well is referred to as PTA-corrected permeability log. Some cored wells were originally exempted from the neural-network permeability modelling because they didn't have logs (sonic, density and neutron logs). Hybrid permeability logs were derived from a combination of the predicted permeability logs and core permeability at these well locations.
All permeability correction logs (i) PTA-corrected permeability logs and (ii) Hybrid permeability logs were then fed back into the geo-modeling workflow to generate an improved permeability distribution which respects core data, PTA kh, and CRRs.
The do-nothing simulation run has more than 80% of wells’ pressure data acceptably history matched. This application demonstrates that a priori integration of dynamic elements like CRR, PTA kh, and the use of CCR-based permeability modeling results in a better characterized geo-model with potential for eliminating the need for history matching.
Insights into the Extra Heavy Oil Property and Oil Charging Combing Rock Pyrolysis Paramenters, Geochemistry Data and Seismic Multi-Attribute Transformation
Authors: Wang Bingjie , Xu Changgui , Wu Kui , Zhang Rucai , Deng Jun , Guo Naichuan 
Published: April 2019

Abstract
A new oil property identification parameter (Pw) is derived which represents the total hydrocarbon generation and pyrolysis hydrocarbon. Using continuous measurement data (Pw) and a series of sample-based attributes from 3D seismic, a strong linear trend is observed. This trend linear is used to calculate heavy oil property data in 3D volume. At the same time, we have got the relationship between reservoir physical and oil properties. Based on this, the core data and geochemical data are used to study the charging of crude oil.
Integrated Geology Sweet Spot and Microseismic Monitor to Optimise Reservoir Stimulation - A Case for Shale Gas, China
Authors: Liu Wei , He Zhenhua , Cao Junxing , Zhang Jianjun , Xu Gang , Wan Xiaoping , Yu Gang 
Published: April 2019

Abstract
Shale play, as one kind of non-conventional natural gas resource, has become the focus of domestic and overseas research in recent years. shale is pertained to be a reservoir with an ultralow porosity and permeability, its occurrence mode, accumulation pattern of natural gas, as well as development model are remarkably different conventional oil gas reservoirs, its development must be implemented by some special technique, e.g. horizontal drilling, drilling geologic steering, hydraulic fracturing, microseismic monitor, etc. Research shows, shale gas production depends on two factors, one is geological sweet spot factor, e.g. Total organic carbon, brittleness, core fluid pressure, micro-fractures, high quality shale thickness etc., the other is engineer technique factors, e.g. horizontal drilling, drilling geologic steering, hydraulic fracturing, microseismic monitor, fracturing schemes, etc. Single factor, sweet spots or engineering technique factor often not guarantee shale gas highly production, only when the most optimal combination of both, can achieve shale gas production maximization. How to integrate sweet spots and engineering technique to guide shale gas exploration and development? This is a serious question. This question involves to many fields, including geology and engineer sweet spots, horizontal well location deployment, drilling geologic steering, pre-fracturing warning and design, hydraulic fracturing design real-time adjustment, microseismic monitoring, etc., sweet spots results runs through the entire shale exploration and development. In this article, we will pay more attention to demonstrate that how to adjust the fracturing scheme and optimize the reservoir stimulation in real time by integrated geological sweet Spot and microseismic monitor.
Improving the Effective Supporting and Fracturing Technology is the Key to the Successful Stimulation of Low-Permeability and Low-Rank Coalbed Methane Reservoirs
Authors: Wang Xin , Zhu Qingzhong , Zheng Wei , Lu Haibing 
Published: April 2019

Abstract
China has abundant low-rank coalbed methane resources. The research object is the low rank lignite seams in Jiergalangtu Sag in Erlian Basin. The reservoir has low porosity and low permeability, and it has no natural productivity. The coal seams have a burial depth of 200-600 meters, thickness of 40-60 meters, and Ro of 0.32% to 0.47%. Borrowing the idea of well completion experience for the conventional low rank coal seams in the region, open hole cavity completion techniques were adopted in two wells, obtaining an output of only about 150 m3/d. The conventional active water fracturing was also tested in another well, and the output after fracturing was 200-300 m3/d. The effect of stimulation was very poor, which limited commercial exploitation activities in the region. This paper introduces two techniques to improve the effect of stimulation by improving induced fracture extension and supporting capacity in the coal seams, including the hydraulic blasting & grouting caving fracturing technique and the reverse compound fracturing technique, which were applied in two wells. A constant rate of production after fracturing reached 1,500-2,000m3/d, which was well above the lower limit output of economic exploitation in the region of 600m3/d. Exciting results were obtained. The exploration of these techniques is of great significance for low rank coalbed methane stimulation, which can help us to implement effective fracturing stimulation operation in low rank coal seams to obtain the best production effect.
Less Damaging Drilling Fluids: Development and Lab Testing
Authors: Alkhalaf Sara , Alawami Mohammed , Wagle Vikrant , Al-Yami Abdullah 
Published: April 2019

Abstract
The properties of the selected drilling fluid must be carefully planned to have minimal effects on the near-wellbore pore spaces. Proper mixing, monitoring, and maintenance of the drilling fluid throughout the drilling operations are as critical as the careful planning. Solids control equipment should be operated to remove the cuttings and maintain the density and rheological properties consistent.
The characteristics of an effective reservoir fluid system include stability at high pressures and temperatures, proper and stable density, good filtration control, ability to transport cuttings, and minimal damage to formation pore spaces, Davidson et al. 1997. Selection of the most suitable drilling fluid additives takes into consideration numerous factors such as downhole conditions (pressure and temperature), formation type and petro physical properties, and the objective of the drilling operation.
The experimental work in this paper involved rheological properties, thermal stability, API and HT/HP filtration and acid filter cake removal efficiency.
Tangentional flooding showed that water based Mn3O4 drill-in fluid has the highest return permeability compared to the typical drill-in fluids (KCl/CaCO3/Barite and potassium drill-in fluids). Potassium formate drill-in fluid filtrate was not compatible with brine. This incompatibility explained its low return permeability in spite of its low solids content. Oil based drilling fluid was developed and tested with good acceptable results. Filter cake removal efficiency was showing more than 95%, indicating its removable formation damage.
Molecular Simulation of Methane Adsorption Behavior in Kerogen Nanopores for Shale Gas Resource Assessment
Authors: Cao Jinrong , Liang Yunfeng , Masuda Yoshihiro , Koga Hiroaki , Tanaka Hiroyuki , Tamura Kohei , Takagi Sunao , Matsuoka Toshifumi 
Published: April 2019

AbstractIn this paper, we present an improved method to predict the methane adsorption isotherm for a real shale sample using molecular dynamics (MD) simulation with a realistic kerogen model. We compare our simulation results both to the experiment and to the simulation results on the basis of a simple graphite model, and show how our procedure leads to the creation of more accurate adsorption isotherms of a shale sample at a wide range of pressure. A Marcellus shale sample was chosen as an example to demonstrate how to calculate the adsorption isotherms using MD simulations. Type II kerogen molecular model was selected for the dry gas window. The constructed bulk kerogen model contains mesopores (&gt; 2 nm) and micropores (≤ 2 nm) inside. Ten different mesopore sizes of kerogen nanopore systems were constructed. According to the characteristics of methane density distribution in the simulation system, three regions can be clearly distinguished, free gas, adsorbed gas, and absorbed gas. We show that the adsorbed gas per unit pore volume increases with the pore size decreased. This is similar to previous molecular simulations with graphite model. For predicting the total adsorption isotherm of a real shale sample, both adsorbed and absorbed gas were considered. For the adsorption amount, the calculated adsorption isotherms were averaged based on pore size distribution of that Marcellus Shale sample. For nanopores smaller than 5 nm, we used total organic carbon (TOC) data to weight the absorption contribution in the kerogen bulk (i.e. inside the micropores). The total adsorption isotherm thus obtained from our simulations reproduced experiments very well. Importantly, kerogen model has overcome the difficulties of prediction using graphite models (i.e. an underestimation of adsorption under high pressure conditions) as documented in previous studies. Furthermore, we predicted the adsorption isotherms for higher temperatures. With the temperature increased, lower adsorption amount is predicted. The novelty of our improved method is that it is able to predict methane adsorption isotherm at a wide range of pressure for a shale sample by considering both adsorption in kerogen mesopores and absorption in kerogen bulk. It can be readily used for any shale sample, where the pore size distribution, porosity, and TOC are known. We remark that the above results and conclusion resulted from our simple assumption. Further discussion might be necessary.
Reviving Idle Wells and Unlocking Potential Production Gain in Offshore Sarawak Through Exposing BCO-LRLC Opportunities
Authors: C A Razak M Amin Nizar , Kamarul Zaman M Naquib , Ali Affira , M Shah Jamari , Sakdillah M Zaki , Md Zainuri Md Zarin 
Published: April 2019

AbstractBrownfield in Balingian and Baram Delta have handful of idle wells and well to be abandoned in their inventories. The project aims to reduce the idle well inventories and support production gain through monetizing behind casing opportunities. The target is to appraise and develop LRLC potentials with lower cost of appraisals. This will maximize full field potentials before abandonment and leads to future development of LRLC opportunities as conventional reservoir becomes more difficult to develop.The idle well inventory has grew up due to problem in production (increase water cut, HGOR) and well problems (sand, fish). An order has been introduced to reduce the idle well list up to 50%. Additionally, in the past, the LRLC intervals were often ignored and considered as water-wet sands due to high water saturation or as tight sands. These intervals, that contain significant reserves, are recognized in many technical papers explaining its identification and evaluation techniques from well-data (logs and samples/cores). The scope of the project is to rejuvenate the idle wells by add-perf LRLC reservoirs.It is impossible to achieve the target without the presence of proper and improved LRLC BCO evaluation process, thus an integrated workflow approach (between Petrophysicist, Reservoir Engineer, Production Technologist, Asset manager & Well Intervention group) has been developed and applied in the project. A new evaluation tools had also been developed called REM (Resolution Enhanced Modelling) in order to improve the log properties of LRLC reservoirs so that the data obtained from old conventional tools can still be used to evaluate LRLC reservoir. Although LRLC is termed UNSEEN, the risk is reduced by proper understanding of hydrocarbon column and sand development.To date, 7 fields are already benefitted from this approach. Field A LRLC reservoir for example has tripled the hydrocarbon saturation, and net to gross has improved to 20% using REM compare to 5% without REM. The other 6 fields are also gaining the same increase in the properties. This has resulted in a cumulative potential of 4.4 MMstb of reserves addition and ~11 KBopd potential gain. As a result, a better and attractive BCO proposals can be generated from LRLC opportunities. The exercise will provide the company with cheaper options of appraising and developing LRLC reservoir while reducing the idle wells. There is no better way of understanding LRLC reservoir; as no tools can identify & quantify it yet, rather from the actual production.
Accelerate Non-Metallic Seal Development for Downhole Applications by Applying a Big Data Method in Material Data
Authors: Huang Xiaobin Betty , Furlan Wayne 
Published: April 2019

Abstract
Non-metallic materials such as plastics and elastomers are widely used for sealing in downhole operations of the oil and gas industry. These materials are used because of their inherent elasticity and resilience over a wide range of deformation, and their non-corrosive nature compared to metals. Downhole non-metallic seal development is an exceptionally complicated task with huge uncertainties due to the hostile operating environment and service longevity. In addition, the field operation is expensive; failure of the downhole seals could cost millions of dollars. Therefore, prior to the release of a non-metallic seal product for downhole applications, extensive material tests should be done in every step in its development from the material selection/development, seal design, seal manufacture to the quality control of the final product to ensure the seal could be safely used in downhole conditions for the service time.
This paper presents a big data method based on tremendous amount of material data to accelerate the downhole seal development. Traditional seal development starts with previous experience to select the seal material and relies greatly on the engineering tool test to validate and optimize the seal design. In contrast, the materials big data method can speed up the development cycle, reduce the uncertainty of the design, minimize the engineering hours spent on data search, guarantee the source and qualify of data used, eliminate redundant testing, and secure the data storage and sharing, etc. Our business experience showed that the materials big data method could reduce 50% human resource while achieving multiple times of productivity.
The Use of Drones for Innovative Seismic Acquisition: A Change of Paradigm for HSE
Authors: Masoni Isabella , Pagliccia Bruno , Thalmann Guillaume 
Published: April 2019

Abstract
The use of drones in the oil and gas industry is still relatively recent, and is currently unlocking new methods and approaches of geophysical acquisition and subsurface imaging. METIS®, a disruptive and integrated research project, employs the use of drones to perform an innovative 3D high density geophysical acquisition, that targets hard-to-access acreage. The benefits associated with the use of drones are easily recognized: an increased efficiency, fewer man hours, reduced HSE risks, and a lower environmental footprint. However a number of new safety, security, regulatory, and public perception issues are raised and need to be better understood before the use of drones can become standard practice. The acceptability of drones and a new method to assess the risks associated to METIS® drone operations is investigated. This study presents how the use of drones is changing the HSE risks associated with an onshore geophysical acquisition, but also how this technology brings new solutions to reduce them.
Eagle Ford Well Insights Using Data-Driven Approaches
Authors: Luo Guofan , Tian Yao , Sharma Akash , Ehlig-Economides Christine 
Published: April 2019

Abstract
Eagle Ford shale in South Texas is a major oil and gas production play of in the US Gulf Coast region. While some attribute the successful well performance of Eagle Ford to the technology advancement such as horizontal drilling and hydraulic fracturing, others credit the role of geological settings. However, it is still unclear what the individual or combined effects from these two sides are. Data-driven approaches, including Partial Least Square (PLS), Random Forest (RF), and Deep Neural Network (DNN), reveal relationships among the production, geological settings, and completion strategies.
In this study, we considered six-month cumulative oil production as the well performance criterion for horizontal wells completed from 2015 to 2017. We selected completion parameters such as perforation length, proppant loading, and fluid volume. We selected structural depth, lower Eagle Ford Shale thickness, total organic carbon (TOC), number of limestone beds, and average bed thickness as the key geological controls on regional production.
We calculated Spearman correlation coefficients to detect correlated input parameters and applied Singular Value Decomposition (SVD) to identify redundant input parameters. Then we performed partial linear square (PLS) regression to predict the six-month oil production from geological and completion parameters. We then used random forest (RF) and deep neural network (DNN) as non-linear machine learning techniques to predict six-month oil production and compared the prediction accuracies for these techniques against the recorded well performance using the coefficient of determination and mean squared error as criteria. Last, we ranked the relative importance of each input parameter using RF and Minimum Redundancy Maximum Relevance (MRMR).
This paper first provides the rational of input variables selection. Then the construed model helps understand the effects of completion designs and geological variables on well productivity in the Eagle Ford. This might provide valuable information to help to make decisions for new well development. This concept can be generalized among other plays.