bluuebunny/crossref_metadata_2025 huggingface dataset:
Reservoir Fluid Geodynamics for Characterization of Reservoir Connectivity: Case Study from Western Kazakhstan
Authors: Ramatullayev Samat , Charupa Mikhail , Blinov Vladislav , Valiakhmetov Rustem , Mendybayev Nurhat , Mukhametrakhimov Shaikhan , Saparova Saule
Published: October 2018

Various techniques are used in industry to determine the reservoir connectivity. The rock-based methods such as seismic mapping and well-to-well correlations present some challenges in subsalt and salt overhang deposits. While fluid based methods such as pressure gradients are traditionally used to infer the lack of connectivity and oftentimes cannot confirm the presence of it. Recent advances in asphaltene science have made it possible to clarify asphaltene distribution mechanism in reservoir allowing to address reservoir connectivity.

The asphaltene distribution in the reservoir is characterized by an equation-of-state (EoS) proposed by Flory, Huggins and Zuo. The classical approach of estimating an asphaltene gradient of the reservoir fluid involves acquiring downhole samples and performing laboratory analysis. However, since the asphaltene content is associated with the coloration of hydrocarbons, a method has been proposed to delineate the asphaltene gradient at reservoir conditions by measuring the color differences of formation fluid with depth using optical fluid analyzer. Integrating the color data from optical fluid analyzer with the proposed equation-of-state, it is possible to elucidate the connectivity of the individual layers.

This paper presents the results of the well logging campaign conducted in the field located in Western Kazakhstan, where new oil deposits were discovered below the salt overhang in Triassic and Permo-Triassic sandstone formations. Several exploration wells were drilled in different blocks of the field. Advanced open hole logging suite comprised of standard logging methods, nuclear magnetic resonance and wireline formation tester was run in all the wells to characterize the reservoir. The wireline formation tester toolstring included an optical fluid analyzer module, which allowed to measure optical density values of an oil at different depths in each of the wells. The study shows how the asphaltene gradient of an oil column constructed from optical density data in conjunction with geological and petrophysical data can be used to delineate reservoir attribute such as lateral connectivity. The analysis confirmed the hypothesis on the lack of reservoir connectivity between the two blocks separated by faults.

The prediction of the asphaltene gradient based on the previously acquired data offers a new way to optimize wireline logging by comparing the predicted with the actual color gradient in real-time during downhole fluid analysis stations, while the tool is still in the well, to uncover the unknown source of reservoir complexity.
Completions and Well Spacing Optimization by Dynamic SRV Modeling for Multi-Stage Hydraulic Fracturing
Authors: Sen Vikram , Min Kyoung Suk , Ji Lujun , Sullivan Richard
Published: October 2018

This paper presents a workflow supported by field examples for modeling the Stimulated Reservoir Volume (SRV) as a Dynamic entity – constrained by validation or calibration against data from the frac treatment, flowback, production and pressure build-ups. The initial reservoir model of static properties is treated as a starting point and evolution of flow and storage are modeled as continuously variable properties using simultaneous modeling of flow and geomechanics – and hence the "dynamic" qualifier for SRV in the title of this paper. This workflow establishes the interaction between reservoir properties and completion parameters – and allows for program optimization. Emphasis is laid on (i) calibration with multiple types of independently measured field data points and (ii) construction of the simplest models which provide a useful degree of predictability, and we caution against over-parameterization through needless complexity of physical models. We first describe the calibration process – during which a model is perturbed to match various aspects of field data, and then explain the prediction process – where multiple hypothetical completion scenarios can be modeled. The results are then screened against economic metrics (e.g. completions costs vs. projected revenue from improved production) – reducing the number of hit-and-miss experiments in the field. While this paper is comprehensive and self-sufficient in its own coverage, it does focus on the physical modeling and calibration aspects, whereas a companion paper (Min et al. 2018), goes into greater detail about the predictive aspects and data analytics driven completion optimization.
Verification Technique of Technical Efficiency of Physical-Chemical EOR
Authors: Morozovskiy Nikita , Kanevskaya Regina , Ulmukhametov Dmitriy , Sergeychev Andrey , Demiyanov Andrey
Published: October 2018

The deterioration in the structure of stocks and the need to maintain the level of oil production leads to an ever-increasing use of tertiary methods of oil production. At present, the most popular of whom PJSC "NK Rosneft" is the physico-chemical methods for increasing oil recovery (PH-CH EOR).

The application of such methods has shown its effectiveness and is actively scaled at various reservoirs and stages of field development.

The variety of objects on which these technologies are applied required the creation of a uniform methodology for verifying the technological efficiency of the PH-CH EOR. This technique is considered in this paper.

The methodology was based on the generalization of the experience accumulated in the Company in the field of application of technologies of physicochemical methods of increasing oil recovery.

After summarizing the results, a transition was made to the estimation of dimensionless parameters that characterize the technological efficiency (TE) of PH-CH EOR measures. This approach allowed us to cluster implementation objects by the effectiveness of various methods.

The generalization of the results of the implementation of the PH-CH EOR also allowed the development of a unified methodology for the calculation of technological efficiency, which became possible to formalize and, accordingly, to automate. Automation of the process of calculating technological efficiency allowed it to unify the majority of the Company's subsidiaries and significantly accelerate, which in turn made it possible to concentrate on selecting candidates and more efficient technologies, rather than on mechanical calculations.
Delivering Drilling Automation II – Novel Automation Platform and Wired Drill Pipe Deployed on Arctic Drilling Operations
Authors: Israel Riaz , McCrae Doug , Sperry Nathan , Gorham Brad , Thompson Jacob , Raese Kyle , Pink Steven , Coit Andrew
Published: October 2018

This paper presents a case history of drilling automation system pilot deployment, inclusive of wired drill pipe on an Arctic drilling operation. This builds on the body of work that BP (the operator) previously presented in 2017 related to the deployment of an alternate drilling automation system. The focus will be on the challenges and lessons learned during this deployment over a series of development wells.

Two major aspects of technology were introduced during this pilot, the first being a drilling automation software platform that allowed secure access to the rig's drilling control system. This platform hosts applications that interpret the activity on the rig and issue control setpoints to drive the operation of the rig's top drive, mud pumps, auto driller, drawworks, and slips. The second component introduced was a wired drill string, which provides access to high speed delivery of downhole data from a series of distributed downhole sensors, providing an opportunity to improve both automated control and real-time interpretation of downhole phenomena.

The project team identified several key performance indicators both at the project level and for each well. The project level key performance indicators (KPIs) were designed to give the operator an understanding of the reliability and robustness of the hardware and software components of the automation system. The KPIs for the well were designed to assess the impact of the technology on drilling efficiency through aspects of invisible lost time reduction (connection and survey times). The well level KPIs also fed into the project KPIs by capturing uptime, reliability, and repeatability of the hardware and software components of the system.

The paper describes several specific examples of where the benefits of the technology were realized as related to the KPIs above and describes some of the technical challenges encountered and fixes employed during the pilot campaign.

The paper also gives an insight into some of the non-technical challenges related to deployment of this system, around human behavioral characteristics. It discusses how focused collaboration and communication from all the stakeholders was managed and directed towards a successful deployment.

The work delivered on this project incorporates several technological innovations that were deployed for the first time on an active drilling operation. Delivery of these were important milestones for both the operator and the automation technology provider as part of their collaboration to increase the capability and reliability of these systems. The operator believes that this effort is key to allowing its drilling operations to realize longer term and sustainable benefits from automation.
Valuation of Government Incentives for Hydrocarbon Asset Developments - A Real Option Approach
Authors: Aliaga Wilson
Published: October 2018

In recent years, the Oil & Gas industry has struggled due to the low hydrocarbon prices. In this context, E&P firms have responded by optimizing their asset portfolio with focus on prospects with the lowest possible financial and technical risks. Since this process has seriously affected countries with emerging hydrocarbon plays and/or relatively low explored geology, government incentives play a pivotal role to mitigate this pessimistic trend. To understand their benefits, this paper presents a methodology to value hydrocarbon prospects using a real option formulation that properly incorporates government incentives such as royalty relief and royalty rate structures that vary with production volumes and prices.

This work links valuation and incentives by using functions that depends linearly on relieved and produced volumes and non-linearly on price. These variable royalty schemes behave similar to bull spreads (financial derivative tool), limiting the upside and downside risk of both government and company take due to hydrocarbon prices and production volumes. Then, we incorporate these functions into the general real option formulation and numerically solve the yielded equations using finite differences methods.

For illustration purposes, the proposed approach is applied to a hypothetical field with an exponential decay production curve. We compare the impact of government incentives for both the prospect value and the optimal investment rule. As observed, these incentives have two merits: to positively influence the economic attractiveness of future developments by compensating investors for their high-risk exposure and to allow an optimal government take in an unfavorable price environment.

We observed that before lease expiration, the real option approach assesses the timing option. Consequently, this approach yields higher values than the NPV analysis since delaying the project execution can be more valuable than the immediate option exercise. Moreover, depending on the financial and volume parameters, intermediate waiting areas might appear between exercise regions. Hence, the methodology proposed captures not only the flexibilities available for an E&P firm that holds a lease, but also the economic benefit of the incentives and their impact on the investment rule.

In summary, the novelty of this work is to propose a general methodology that includes government incentives in the economic analysis of hydrocarbon asset developments using real options. This methodology captures the flexibilities available for managers to respond to hydrocarbon price uncertainty. Moreover, we also propose a new royalty rate scheme that resembles bull spreads. This scheme allows investors to capture the upside of production and prices, and optimizes the government take.
Sweep Efficiency Increasing of Fractured Oil-Wet Reservoirs: Case Study of Central Khoreyver Uplift Fields
Authors: Yudin Evgenii Viktorovich , Chorniy Aleksei Vadimovich , Churanova Nina Yurevna , Soloviev Aleksei Vitalevich , Khairullin Marsel Mansurovich , Sadreev Ernest Anvarovich , Yushmanov Aleksei Igorevich
Published: October 2018

Reserves decline of oil fields with fractured carbonate reservoir is crucially complicated due to formation vertical heterogeneity, occurrence of separate filtration pathways and as a consequence poor sweep efficiency of reservoir development both in vertical and lateral direction. As a result – there is a failure to achieve expected level of recovery factor.

To its simplest extent there is no clear understanding of mechanism of reserves recovery, quite how the process of oil displacement happens: either in vertical direction or in a layered way. More uncertainties in the research process are brought by active aquifer, reservoir pressure maintenance system directly below the OWC and nonuniform recovery at vertical scale.

The results of systematic survey of sweep efficiency increasing of oil-wet fractured carbonates are reviewed on the article using the case study of Central Khoreyver Uplift fields (CKU) of LLC «JC «RUSVIETPETRO» company.
Application of Machine Learning Methods for Modeling the Current Indicators of Operating Wells Stock of PJSC Gazprom Neft
Authors: Teplyakov Nikolay , Slabetskiy Andrey , Sarapulov Nikolay , Simonov Maksim , Perets Dmitriy , Kotezhekov Viktor , Andrianova Alla
Published: October 2018

The main goal of the work is to review and describe the algorithms currently being developed in order to obtain reliable well operation parameters using machine learning (ML) methods, existing constraints and results achieved. The work covers a number of ML applications. The methodology of bottomhole pressure modeling is described, which, in the absence or in case of failure of pressure sensors at an ESP inlet, allows more reliably, in comparison with a conventional approach, to determine the bottomhole pressure in wells and thereby improve the efficiency of selecting well candidates for well interventions. To assess well flow rates, another key indicator, an ESP instant flow ate simulator has been developed that allows obtaining information about the instantaneous production rate at any time, thus increasing the accuracy of cyclic wells measurements, and promptly implementing the necessary corrective measures. Approaches to modeling the impact of injection on the production well rates have also been considered.
Deterministic or Probabilistic Approach for Mature Oilfield Development: Isn't it all about Decision Making?
Authors: Yadav Atul , Omara Essam , El-Hawari Ahmed , Malkov Anton , Bisso Bi Mba Edna Michelle
Published: October 2018

The integrated reservoir management study was initiated to identify the best fit for purpose dynamic model for decision making with increased confidence in production forecasts and proposed field development plan. The mature oil field with complex geology and huge uncertainties in production allocation is located in the Gulf of Suez. These conditions made it necessary to evaluate and select the best suited approach between the deterministic and probabilistic approach.

Considering the geological complexity and field performance behavior, integrated reservoir modeling approach was selected to achieve objectives of the study. During that process, the screening for the best suited methods such as probabilistic or deterministic was made. Due to the heavily faulted and compartmentalized nature of the field with an incomplete understanding of probability ranges and relationship between different variables none of the approaches is perfect.

The acceptable field history match was achieved to serve the basis for future field development and to increase the current production rates. The chosen deterministic approach illustrates how the global and regional uncertainties can be narrowed down by consequent analysis and inclusion of observed well data such as RFT, PLT, production water salinity, tracer data etc. The accurate estimation of remaining production potential in different reservoir units was delivered. The promising prospects in the eastern blocks adjacent to the main producing field were identified. The estimated incremental oil production within next 5 years is around ~10 MMSTB. Above mentioned results were considered as a basis for the license extension which was about to expire in the year 2017. This integrated approach was helpful for understanding the difficulties in complex oil field simulation study, knowledge sharing between management of joint partners, G&G experts, and reservoir engineers.

The upstream industry projects demand the optimal solutions for business decisions under prevailing uncertain conditions. The presented case study provides a practical approach to achieve it. The lessons learned from the study may give a guidance to similar mature field development in the region and worldwide.
Increased Oil Production in Super Thin Oil Rim Using the Application of Autonomous Inflow Control Devices
Authors: Mohd Ismail Ismarullizam , Che Sidik Nor Azuairi , Syarani Wahi Faez , Tan Giok Lin , Tom Focht , Hillis Frazer
Published: October 2018

Despite the improved productivity and uplift in reserve recovery associated with horizontal wells, reservoir heterogeneity can cause uneven production, and early water and gas breakthrough from portions of the wellbore. Inflow control devices (ICD) create additional pressure drop to balance the production flux, but cannot restrict unwanted effluents once they break through.

The Autonomous Inflow Control Device (AICD) actively delivers a variable flow restriction in response to the properties (viscosity) of the fluid flowing through it. Water or gas flowing through the device is restricted more than oil. When used in a horizontal well, segmented into multiple compartments, this design prevents excessive production of unwanted effluents after breakthrough occurs in one or more compartments.

In the 2016 infill development campaign, production was improved by AICDs to ensure contribution from all reservoir sections, and limit gas and water production by postponing breakthrough and restricting unwanted effluent production after breakthrough.

A nine well program was selected to demonstrate the effectiveness of AICD completions in the East Belumut and West Belumut assets, a field development offshore Malaysia. The wells are drilled with horizontal lengths typically 1.5km within the original 6-8m thin oil column for West Belumut and 10-14m thin oil column for East Belumut. The program comprised of AICD flow loop testing, performance modelling, candidate selection, completion design and comparing production results with neighbouring ICD wells in the fields.

The implementation of an AICD completion was a success and full fields implementation took place in 2017. First installed in March 2016, the AICD completion was adopted as part of the standard lower completion solution at East Belumut. To date additional wells have been completed with AICD completions in East and West Belumut fields, demonstrating significant increase in cumulative oil production, reduction in GOR of the AICD wells by 50%, and achieving 50% more oil production compared to offset ICD wells.

This paper describes a full field implementation for the application of AICDs in a super thin layer, oil reservoir offshore Malaysia. Nine new horizontal wells in two different fields were completed with AICDs to reduce the amount of water and gas production from these wells and to enhance the reserve recovery from the asset. The paper describes the workflow for establishing the suitability of candidates for the technology, the completion design process, and the enhanced production results of the program after 2 years production.
Cognitive Analytical System Based on Data-Driven Approach for Mature Reservoir Management
Authors: Gopa Konstantin , Yamov Sergey , Naugolnov Mihail , Perets Dmitrii , Simonov Maksim
Published: October 2018

The work tells about the experience of development and application of the cognitive analytical system prototype based on the approaches and principles of machine learning for the processing and analysis of statistical and dynamic data collected during the field exploitation. The developed system prototype implements an efficient and modern data-driven approach to field development management, and is an example of a paradigm shift in reservoir modeling.

In the process of creating a prototype of a cognitive analytical system for managing field development, both classical machine learning methods and modified capacitive-resistive models (CRM), INSIM models were used, as well as the approach of identifying a nonlinear dynamic system through recurrent neural networks (RNN). This system provides rapid analysis of a mature field development and to handle such management tasks as optimization of the water flooding, identification of inefficient injection and production, evaluation of the wells interaction, forecast of liquid and oil production at a given time horizon. The system consists of the models that have weak points and limitations, and is not an ultimate decision-making system, but should be used in conjunction with other tools for reservoir engineering. This tool will be an addition to the approaches of the "Digital Field", in which the field development is optimized not only based on the results of the classic physics models, but also on machine learning technologies applied to the entire development history.
Enabling Real-Time Asset Analytics for a Cloud-Based Fiber-Optic Data Management System
Authors: Yang Lei , Bale Derek S. , Yang Don , Raum Matthew , Bello Oladele , Failla Roberto , Lerohl David , Knowles David , Kwari Andy , Cannon Matt , Ye Shuzhen
Published: October 2018

The distributed nature of fiber-optic measurements such as distributed temperature sensing (DTS), distributed acoustic sensing (DAS), and distributed strain sensing (DSS) enables nearly continuous monitoring of the downhole environment in both space and time. Though continuous monitoring opens the door to a rich new set of asset management applications, it comes with its own set of challenges in terms of data transmission, management, and security. Recently, cloud-based fiber-optic data management services have been successfully introduced to the oil and gas industry as an effective way to collect, transfer, store and display distributed measurement data from the downhole environment. To maximize the value of such cloud-based data management systems, and further improve the return on investment for asset managers, the large volume of distributed sensing data collected must be converted to value in a simple and easy-to-use form, depending on different applications. Traditionally, interpretation of distributed sensing data is a manual process conducted by engineers in a post-job workflow. This paper presents the successful integration of an analytics library into the cloud-based fiber-optic data management system. This integration enables real-time, and in some cases near real-time, asset decision making. The design of the analytics architecture is open to meet the wide range of application requirements by asset managers. A few application examples of the analytics integration will be presented using real-time data streamed directly from the field.
Automatic Geosteering of Wells
Authors: Stishenko Sergei , Petrakov Yuri , Sabirov Airat , Sobolev Alexey
Published: October 2018

The issue of automatic transfer of well data was successfully resolved long time ago by means of WITS\WITSML protocols, and now automatic processing of the received data is on the agenda. The article describes the basics of automatic well geosteering, cross-check of the results of automatic geosteering performed by specialists, conceptualizes a new workflow and proposes scenarios for further development of the discipline.
Machine Learning Approach to Open Hole Interpretation and Static Modelling Applied to a Giant Field
Authors: Ismagilov Amir , Sudakov Vladislav , Nurgaliev Danis , Murtazin Timur , Usmanov Sergey , Nugumanova Natalya
Published: October 2018

This paper introduces description and results of creating a complex method for automatic interpretation of well-logging data and the further construction of the first approximation of the geological model. That procedure is aimed at mass re-interpretation of a large number of wells in terrigenous deposits of Tula and Bobrikovian horizons of Tatarstan. It was solved with the use of machine learning methods and artificial neural networks. We also proposed an improved solution to the problem of determining the reference value for normalization of neutron logging in the absence of reference horizon data. The priority task of well logs depth matching is solved by use of correlation coefficient, logistic regression and the idea that the expert has a different preference to different depth intervals in the investigated horizon. The next issue is the stratigraphic division which also was solved by logistic regression training as the most common tool in allocating one object from others and alternately defining each boundary of deposits. Solution for the problem of the reference maximum value of neutron logging in the absence of a reference Famennian stage was established by revealing the algorithmic relationship between the boundary values of the porosity for the reservoirs of the underlying carbonate reservoirs and the calculation of the porosity from the normalized neutron log. Further question of determining the lithology is solved with the use of two-layer perceptron and an expanded use of well log values, that is, a two-meter interval of curve values was chosen for interpreting each point in depth, which allowed to take into account the change zones of rocks and include shape of the curves in addition to their quantitative indications. Calculation of porosity and the oil saturation coefficient includes already existing dependencies for the processed objects of Tatarstan, therefore the accuracy of the calculation depends only on the quality of the determination of the reference values of radioactive and neutron logs, which was successfully carried out. The final goal in obtaining such a large volume of results of automated calculations for wells is the construction of a geological model, where a detailed correlation, grid construction and interpolation of petrophysical parameters on the grid are carried out.
Case Study of Digital Oilfield Implementation in the Southern North Sea
Authors: Elichev Vitaly , Muñoz Esteban
Published: October 2018

During times of volatile oil & gas prices, optimization of exploration, development and production costs per barrel of oil equivalent is the critical component for international upstream companies to ensure competitiveness and profitability.

Recently many companies have invested in data gathering systems, laboratory experiments and databases. Currently high amount of information is being gathered and stored in various databases and files. Therefore, a rapid, effective and smart information processing is crucial in decision making to maximize value of information and the asset performance. Considering field dynamic, processing the information real time generates value for maximizing key performance parameters and support on a day to day basis the complete production system.

The technology, which helps to process the information in operational conditions efficiently and improves operativity and quality of operational decisions is "digital oilfield". Wintershall is actively developing and implementing an own version of digital oilfield. Examples on a field case with focus on the dynamic model requirements in a tight oil field with strict flow assurance requirement, implementation of a ssuperposition principle and hybrid virtual flow meter are presented. This paper describes a case study of digital oilfield implementation in the Southern North Sea.
Optimizing Nanoparticle-Stabilized Emulsion Behavior in Porous Media Through Electrostatic Interactions
Authors: Daigle Hugh , Griffith Nicholas
Published: October 2018

We tested how different emulsion characteristics would affect transport through sandstone cores and recovery of residual oil. Our results show that the behavior of nanoparticle-stabilized emulsions flowing through porous media can be described in terms of filtration theory and electrostatic and van der Waals interactions. Residual oil recovery was enhanced by optimizing em—ulsion characteristics such as salinity, method of generation, and zeta potential. We emulsified widely available, low-cost natural gas liquids in brine using polyethylene glycol-coated silica nanoparticles. Emulsions were generated via sonication at varying salinities and zeta potentials for observations of emulsion characteristics. We conducted corefloods in Boise sandstone to assess the effects of different emulsion properties on residual oil recovery of heavy oils, effective permeability reduction capabilities (i.e. conformance control), and in-situ emulsion stability. Emulsions with high salinity content resulted in better in situ emulsion stability and up to 89% recovery of residual mineral oil at low injection rates. By increasing the salinity, the magnitude of the repulsive electrostatic force between emulsion droplets and grain surfaces is decreased, leading to increased droplet interception on grain surfaces. This results in more extensive droplet-pore throat blockage, redirecting the displacing fluid into less permeable zones. Increasing the magnitude of the droplet zeta potential of injected emulsions marginally increased in oil recovery, significantly reduced permeability, and increased in situ emulsion stability. The best residual oil recovery occurs when emulsion droplets can persist without coalescence under the pressures required to push them into small pore throats, while simultaneously moving through the larger pore throats rather than being mechanically or electrostatically retained. Proper emulsion flood design, therefore, must incorporate characterization of both the pore structure and the electrostatic properties of reservoir rocks and how these will interact with the emulsions.
Improving the Effectiveness of Diverters in Hydraulic Fracturing of the Wolfcamp Shale
Authors: Harpel Jennifer , Ramsey Lee , Wutherich Kevin
Published: October 2018

A project was initiated in the Wolfcamp shale to reduce the operational complexity and costs associated with hydraulic fracturing. The goal was to use dissolvable diverter to increase fracturing stage lengths while maintaining an average cluster spacing similar to the current completion design without affecting well productivity. To ensure maximum effectiveness, a unique methodology was employed that uses reservoir properties along the lateral to create stage specific diverter strategies.

The methodology used to design the diverter strategies begins with understanding well heterogeneity along the lateral. Estimations of minimum in situ stress at each cluster were combined with estimates of pressure increases caused by stress shadow both from previous stages and between treatment clusters to determine fracture breakdown pressures along the lateral. This data was used to selectively segregate the clusters into those that would be treated before diversion and those that would be treated after diversion. Additionally, calculations including hoop stresses and perforation friction were used to ensure pumping pressures remained in a range that increased the probability that clusters designed to take fluid after diverter were not prematurely broken down during the initial pumping treatment.

This approach of engineered diversion was applied to three wells located in the Wolfcamp shale of the Midland Basin. The completion incorporated a designed stage length that averaged 315 ft with nine perforation clusters per stage using a single diversion drop. Typical well designs in this field contain stages that are 175 ft in length with five perforation clusters. Thus, this revised design constituted an eighty percent increase in stage length over conventional stage designs.

The goal of the treatment was to increase stage length without affecting production. During the treatment of the new engineered diverter stages, there was clear indication that after the first portion of the fracture was completed and diverter had seated on the perforations, the fluid was effectively diverted to virgin rock. This is a positive indication that the stage-specific diverter design was effective. Additionally, when comparing production between the three wells in this study and conventionally stimulated offset wells, there was no appreciable difference in production.

This case study represents one of the earliest applications of a fully engineered diversion strategy and will describe how lessons learned during this application can be applied to further improve economics and effectiveness of diverters in horizontal shale plays.
Application of Machine Learning for Oilfield Data Quality Improvement
Authors: Andrianova Alla , Simonov Maxim , Perets Dmitry , Margarit Andrey , Serebryakova Darya , Bogdanov Yuriy , Budennyy Semen , Volkov Nikita , Tsanda Artem , Bukharev Alexander
Published: October 2018

The paper describes the principal possibility of using machine learning methods for verifying and restoring the quality of oilfield measurements. Basic methods for screening incorrect values have been given and approaches for solving three problems have been recommended:

Correctness analysis of well logging data Quality control of physical and chemical fluid properties (PVT-studies) Separation between the base production and effect from well interventions (WI) to predict the performance of hydraulic fracturing (frac).

The main deliverable is a set of algorithms based on machine learning methods, which allows to automatically process large volumes of field data. A number of approaches is proposed, including using modern methods of machine learning, to restore the missing values and the quality of algorithms operation.
Innovative Strategy Decision and Portfolio Assets Analysis Instrument – Permanent Model of On-Line Reserves and Resources Monitoring
Authors: Zaharova Oksana , Teplouhova Irina , Islamuratov Marat , Kisurina Anastasiya , Panfilova Elena , Stepanova Valeriya , Ershov Aleksandr
Published: October 2018

The effectiveness of reserves and resources management depends on the full performance about resource base of the Company, its development history, current status and future prospects. Existing methods of data evaluation and analytics are not interconnected between each other and provided in the different systems by the various specialist's background.

The volume of information is increasing yearly despite production conditions are getting complicated. Extremal climate conditions, remote territories, unconventional reserves are usual circumstances of actual oil and gas industry. The only method to save the cost production level is enhancing business process efficiency.

Historically the Oil and Gas industry has a huge risk of failure – dry well or incorrect development plan may lead to great losses and jeopardize company's sustainability. For the very reason it is very important to increase effectiveness and rationality of the Resource base Management using disparate data source.

In response to the complex needs of the oil and gas industry, it became necessary to use a single software solution for strategic decision making based on resource base analysis. Russian and foreign markets analysis showed the lack of software that is fully complies with the business needs. This fact encourage Gazprom Neft to develop its own IT solution for managing the company's reserves and resources: "Constantly operating reserves and resources model (PDMZiR)". This is a unique tool that aggregate huge amount of information from different data sources and modern technics for Resource base Management in one platform. PDMZiR can significantly increase the quality of resource base estimations for any Oil and Gas company, improve assets potential, identify and prevent any significant risk, justify recommendations for field development.
Determination of Lithologic Difference at the Bottom of Wells Using Cognitive Technologies
Authors: Simon Igor , Koryabkin Vitaly , Semenikhin Artyom , Gruzdev Arseniy
Published: October 2018

In this paper, we present a methodology for determining lithological difference at the bottom of the well during drilling operations. Our approach is based on the analysis of mechanical parameters of drilling. These parameters are receiving as real-time time-series data. The central part of the methodology is a model based on the machine learning approach. Our model and the whole methodology can be applied to real drilling cases. The set of parameters that are required for the methodology can be collected from the typical mud logging station.

The main use case for the methodology is an optimization of the geosteering process. The most modern geosteering approaches are based on the LWD data. It is the main restriction of common approaches for the adjustment of the direction of drilling. The problem is that the LWD sensors are placed for a few decimals meters before the bit in a typical Bottom Hole Assembly (BHA) design. As a result, these a few tens of meters are drilling in a "blind window".

The methodology is illustrated on the historical data of drilling of the Novoportovskoe oilfield. At the current stage, the results of the testing show that suggested methodology can correctly classify two out of three cases of changes of lithotypes while drilling.
Depogrid: Next Generation Unstructured Grids for Accurate Reservoir Modeling and Simulation
Authors: Santoshini Samita , Harris Simon , Kashem Sheleem , Levannier Arnaud , Benabbou Azeddine , Viard Thomas , Macé Laetitia
Published: October 2018

As the oil and gas industry is moving toward tapping reserves in more complex structural environments, there comes the challenge for the reservoir modeling platform to accurately and robustly build and simulate a model to aid in making more trustworthy reserve estimates and field development plans. The 3D geocellular model sits at the core of an integrated seismic-to-simulation workflow, within which one can characterize and predict the behavior of reservoirs and can make confident quantitative decisions about one's assets. In structurally complex areas, the construction of accurate 3D models is often impeded by fundamental limitations of standard geocellular modeling technologies. With these limitations in mind, a new cut-cell unstructured grid has been developed that honors the geological structure precisely, enables accurate property modeling in a flattened, un-faulted, pseudo-depositional space, and can be simulated directly in a next-generation simulator; we call this unstructured grid built in depositional space a ‘depogrid’. The polyhedral, cut-cell nature of the depogrid arises when the regularly gridded volume in depositional space is cut by the structural model features (faults and unconformities) before being forward-deformed into geological space. By construction, the grid cell columns are orthogonal to the local stratigraphy, yet they can accurately represent complex structures and volumes, independent of the grid resolution. A next-generation high resolution reservoir seamlessly consumes the globally unstructured grid topology, with the structural complexity and non-neighbor connections, and honors the flow dynamics accurately. This ensures a more geologically consistent simulation model, realistic parameters to tune for history matching workflows and the ability to make reliable predictions.

We present some particular reservoir modeling and simulation considerations where the depogrid approach improves on typical gridding technologies. The seismic-to-simulation workflow is then applied to several structural examples to demonstrate how the depogrid is best suited to model the geological structure and properties in a variety of reservoirs and subsequently improves the accuracy and efficiency of field development planning and risk mitigation.
Present Day Softskills Needs and Views – 2017 SPEI Survey Results
Authors: Cheung Steve , Rijken Peggy , Agbor Etta
Published: October 2018

This paper documents the results from a SPEI survey that was distributed in October 2017 to gauge the members’ Softskills needs and preferences. We received approximately 1100 responses from 72 countries. This paper shows the survey statistics and shares the actions the SPEI Business Management and Leadership Committee (BML) (previously known as the Softskills committee) will consider in response to the survey results.
Coupled Fluid Flow and Geomechanical Modeling of Seismicity in the Azle Area North Texas
Authors: Chen Rongqiang , Xue Xu , Yao Changqing , Datta-Gupta Akhil , King Michael J. , Hennings Peter , Dommisse Robin
Published: October 2018

A series of earthquakes was recorded along a mapped fault system near Azle, Texas in 2013. To identify the mechanism of seismicity, coupled fluid flow and geomechanical simulation is carried out to model fluid injection/production and the potential onset of seismicity. Sensitivity studies for a broad range of reservoir and geomechanical parameters are performed and the calibrated models are used to identify controlling mechanisms for seismicity in the Azle area, North Texas and its relationship to hydrocarbon production and fluid injection in the vicinity. Geologic, production/injection, and seismicity data are gathered to build a detailed simulation model with coupled fluid flow and geomechanics. Geomechanical simulation results are used to calculate cumulative seismic moment magnitude. Sensitivity analyses for injection well head pressure and earthquake data are performed over a range of reservoir and geomechanical parameters. Influential parameters are selected to perform a pareto-based multi-objective history matching of well head pressures and seismic moments.

Geomechanical interaction has significant impact on seismicity in the Azle area. Unbalanced loading (overall injection and production) on different sides of the fault generates accumulation of strain change, resulting in the onset of seismicity. Previous studies seem to have significantly underestimated the fluid withdrawal rates, almost by an order of magnitude. The equivalent bottom-hole fluid rate used in this study suggests a drop in reservoir pore pressure which is consistent with the BHP trends. Thus, pore pressure increases may not explain the seismicity near the Azle area, as indicated in previous studies. Instead geomechanical effects and strain propagation to the basement appear to be the dominant mechanisms. The low fault cohesion and minimum horizontal stress obtained from history matching suggest that the faults must be near or at the critically-stressed state before the initiation of fluid production/injection. A sensitivity analysis indicates that the minimum horizontal stress and fracture gradient each play a critical role in the potential risk for seismicity related to fluid injection/production. Streamline flow pattern further proves that there is no fluid movement in the basement formation and the unbalanced loading from different sides of the fault is the controlling mechanism. This is the first study coupling fluid flow and geomechanics in the Azle area and the first to simultaneously calibrate the models with fluid flow and seismicity data.
Lunar Drilling – Challenges and Opportunities
Authors: Sawaryn S. J. , Bustin P. , Cain M. G. , Crawford I. A. , Lim S. , Linossier A. , Smith D. J.
Published: October 2018

Plans are being developed to drill and core a 100m deep hole on the Moon, an ambitious step beyond the 2.986m depth achieved at the Apollo 17 landing site in 1972. There are many scientific reasons for doing this, including determining the lunar geology and mineralogy below the meteorite-shattered regolith surface and its volatile content and heat flow, and identifying resources for future human space exploration. The complexities are such that the mission lead time is 15 plus years.

Drilling in the lunar environment poses many different technical challenges compared with terrestrial activity, although there are parallels, starting with the geological prognosis, similar to an oilfield exploration play. Operationally, the drilling equipment must escape Earth's gravitational pull. It must then travel almost 400,000 km to the drill site before it can be deployed and drilling starts. With each Kilogram of payload currently costing over $1.2 Million (Astrobotic Payload User Guide) this limits the available continuous power to approximately 100W, a minute fraction of its terrestrial counterparts. At these distances a radio signal takes 2.5 seconds to make the round trip, so the deployment and subsequent drilling activities must be largely autonomous. To add to the difficulties, the Moon's gravitational pull is only one sixth that of the Earth and it has no atmosphere, introducing weight on bit and hole cleaning problems. Despite the low gravity, displaced solids can still fall and obstruct the hole and some form of casing will be needed to secure the hole as it is drilled. The associated weight, power and geometrical limitations make this another serious technical challenge that must be overcome. With little or no chance to intervene, the operation must aim to be engineered to be right-first-time.

In the last decade, automation in oil and gas drilling has advanced considerably (Veila 2016; Hseih 2017). The number of autonomous activities has expanded and system reliability has improved. This, and other aspects of oil and gas experience, coupled with space exploration expertise and technology will be combined to make this happen. The pay-back for the oil and gas industry's involvement in this endeavor will be a step change in reliability and efficiency that results from satisfying the demanding conditions and clinical attention to detail that will be necessary to succeed.

The paper contains details of the goals, challenges and current thinking that drilling on the Moon entails, and the parallels with oil and gas drilling. This comes at a time when there is a renewed interest in further exploration of our solar system, particularly the Moon and Mars.