Implementation of Vendor-Independent Stochastic Inversion for Improving Quality and Efficiency of Well Placement on the Field of Novatek Company

2021 ◽  
Author(s):  
Danil Andreevich Nemushchenko ◽  
Pavel Vladimirovich Shpakov ◽  
Petr Valerievich Bybin ◽  
Kirill Viktorovich Ronzhin ◽  
Mikhail Vladimirovich Sviridov
Keyword(s):  
Real Time ◽  
Well Placement ◽  
Stochastic Inversion ◽  
Mathematical Algorithm ◽  
Service Companies ◽  
Resistivity Data ◽  
Resistivity Logging ◽  
Azimuthal Resistivity ◽  
Logging Tool ◽  
Single Approach

Abstract The article describes the application of a new stochastic inversion of the deep-azimuthal resistivity data, independent from the tool vendor. The new model was performed on the data from several wells of the PAO «Novatek», that were drilled using deep-azimuthal resistivity tools of two service companies represented in the global oilfield services market. This technology allows to respond in a timely manner when the well approaches the boundaries with contrasting resistivity properties and to avoid exit to unproductive zones. Nowadays, the azimuthal resistivity data is the method with the highest penetration depth for the geosteering in real time. Stochastic inversion is a special mathematical algorithm based on the statistical Monte Carlo method to process the readings of resistivity while drilling in real time and provide a geoelectrical model for making informed decisions when placing horizontal and deviated wells. Until recently, there was no unified approach to calculate stochastic inversion, which allows to perform calculations for various tools. Deep-azimuthal resistivity logging tool vendors have developed their own approaches. This article presents a method for calculating stochastic inversion. This approach was never applied for this kind of azimuthal resistivity data. Additionally, it does not depend on the tool vendor, therefore, allows to compare the data from various tools using a single approach.

Integrating Images From Multiple Depths of Investigation and Quantitative Signal Inversion in Real Time For Accurate Well Placement

2008 ◽  
Author(s):  
Roland E. Chemali ◽  
Michael S. Bittar ◽  
Frode Hveding ◽  
Min Wu ◽  
Michael Raymond Dautel
Keyword(s):  
Real Time ◽  
Well Placement ◽  
Signal Inversion

Using Real-Time Pressure Data for Well Placement Planning

2006 ◽  
Author(s):  
Mike Parker ◽  
Robert N. Bradford ◽  
Laurence Ward Corbett ◽  
Robin Noel Heim ◽  
Christina Leigh Isakson ◽  
...  
Keyword(s):  
Real Time ◽  
Time Pressure ◽  
Pressure Data ◽  
Well Placement

A Novel Workflow for Geosteering a Horizontal Well in a Low Resistivity Contrast Anisotropic Environment: A Case Study in Semoga Field, Indonesia

2021 ◽  
Author(s):  
Yessica Fransisca ◽  
Karinka Adiandra ◽  
Vinda Manurung ◽  
Laila Warkhaida ◽  
M. Aidil Arham ◽  
...  
Keyword(s):  
Real Time ◽  
Horizontal Well ◽  
Gamma Ray ◽  
Lessons Learned ◽  
Electrical Anisotropy ◽  
Anisotropy Ratio ◽  
Well Placement ◽  
Low Contrast ◽  
Low Resistivity ◽  
The Subject

Abstract This paper describes the combination of strategies deployed to optimize horizontal well placement in a 40 ft thick isotropic sand with very low resistivity contrast compared to an underlying anisotropic shale in Semoga field. These strategies were developed due to previously unsuccessful attempts to drill a horizontal well with multiple side-tracks that was finally drilled and completed as a high-inclined well. To maximize reservoir contact of the subject horizontal well, a new methodology on well placement was developed by applying lessons learned, taking into account the additional challenges within this well. The first approach was to conduct a thorough analysis on the previous inclined well to evaluate each formation layer’s anisotropy ratio to be used in an effective geosteering model that could better simulate the real time environment. Correct selections of geosteering tools based on comprehensive pre-well modelling was considered to ensure on-target landing section to facilitate an effective lateral section. A comprehensive geosteering pre-well model was constructed to guide real-time operations. In the subject horizontal well, landing strategy was analysed in four stages of anisotropy ratio. The lateral section strategy focused on how to cater for the expected fault and maintain the trajectory to maximize reservoir exposure. Execution of the geosteering operations resulted in 100% reservoir contact. By monitoring the behaviour of shale anisotropy ratio from resistivity measurements and gamma ray at-bit data while drilling, the subject well was precisely landed at 11.5 ft TVD below the top of target sand. In the lateral section, wellbore trajectory intersected two faults exhibiting greater associated throw compared to the seismic estimate. Resistivity geo-signal and azimuthal resistivity responses were used to maintain the wellbore attitude inside the target reservoir. In this case history well with a low resistivity contrast environment, this methodology successfully enabled efficient operations to land the well precisely at the target with minimum borehole tortuosity. This was achieved by reducing geological uncertainty due to anomalous resistivity data responding to shale electrical anisotropy. Recognition of these electromagnetic resistivity values also played an important role in identifying the overlain anisotropic shale layer, hence avoiding reservoir exit. This workflow also helped in benchmarking future horizontal well placement operations in Semoga Field. Technical Categories: Geosteering and Well Placement, Reservoir Engineering, Low resistivity Low Contrast Reservoir Evaluation, Real-Time Operations, Case Studies

Generation of a Complete Profile for Porosity Log While Drilling Complex Lithology by Employing the Artificial Intelligence

2021 ◽  
Author(s):  
Ahmed Al-Sabaa ◽  
Hany Gamal ◽  
Salaheldin Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Prediction Model ◽  
Real Time ◽  
Storage Capacity ◽  
Data Set ◽  
Drilling Parameters ◽  
Unseen Data ◽  
Rock Porosity ◽  
Data Points ◽  
Logging Tool

Abstract The formation porosity of drilled rock is an important parameter that determines the formation storage capacity. The common industrial technique for rock porosity acquisition is through the downhole logging tool. Usually logging while drilling, or wireline porosity logging provides a complete porosity log for the section of interest, however, the operational constraints for the logging tool might preclude the logging job, in addition to the job cost. The objective of this study is to provide an intelligent prediction model to predict the porosity from the drilling parameters. Artificial neural network (ANN) is a tool of artificial intelligence (AI) and it was employed in this study to build the porosity prediction model based on the drilling parameters as the weight on bit (WOB), drill string rotating-speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q). The novel contribution of this study is to provide a rock porosity model for complex lithology formations using drilling parameters in real-time. The model was built using 2,700 data points from well (A) with 74:26 training to testing ratio. Many sensitivity analyses were performed to optimize the ANN model. The model was validated using unseen data set (1,000 data points) of Well (B), which is located in the same field and drilled across the same complex lithology. The results showed the high performance for the model either for training and testing or validation processes. The overall accuracy for the model was determined in terms of correlation coefficient (R) and average absolute percentage error (AAPE). Overall, R was higher than 0.91 and AAPE was less than 6.1 % for the model building and validation. Predicting the rock porosity while drilling in real-time will save the logging cost, and besides, will provide a guide for the formation storage capacity and interpretation analysis.

Real-Time Simulation of Deep Azimuthal Resistivity Tool in 2D Fault Model Using Neural Networks

2018 ◽  
Author(s):  
Dmitry Kushnir ◽  
Nikolay Velker ◽  
Alexey Bondarenko ◽  
Gleb Dyatlov ◽  
Yuliy Dashevsky
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Fault Model ◽  
Real Time Simulation ◽  
Time Simulation ◽  
Azimuthal Resistivity

Utilizing NMR Workflow to Optimize Power Water Injector Placement in the Presence of Tar Barriers

2021 ◽  
Author(s):  
Gabor Hursan ◽  
Mohammed Sahhaf ◽  
Wala’a Amairi
Keyword(s):  
Spectral Analysis ◽  
Pore Size ◽  
Real Time ◽  
Total Porosity ◽  
Carbonate Reservoir ◽  
Wettability Alteration ◽  
Well Placement ◽  
Carbonate Formation ◽  
Fluid Saturation ◽  
Rock Quality

Abstract The objective of this work is to optimize the placement of horizontal power water injector (PWI) wells in stratified heterogeneous carbonate reservoir with tar barriers. The key to successful reservoir navigation is a reliable real-time petrophysical analysis that resolves rock quality variations and differentiates tar barriers from lighter hydrocarbon intervals. An integrated workflow has been generated based on logging-while drilling (LWD) triple combo and Nuclear Magnetic Resonance (NMR) logging data for fluid identification, tar characterization and permeability prediction. The workflow has three steps; it starts with the determination of total porosity using density and neutron logs, the calculation of water-filled porosity from resistivity measurements and an additional partitioning of porosity into bound and free fluid volumes using the NMR data. Second, the total and water-filled porosity, the NMR bound fluid and NMR total porosity are used as inputs in a hydrocarbon compositional and viscosity analysis of hydrocarbon-bearing zones for the recognition of tar-bearing and lighter hydrocarbon intervals. Third, in the lighter hydrocarbon intervals, NMR logs are further analyzed using a multi-cutoff spectral analysis to identify microporous and macroporous zones and to calculate the NMR mobility index. The ideal geosteering targets are highly macroporous rocks containing no heavy hydrocarbons. In horizontal wells, the method is validated using formation pressure while drilling (FPWD) measurements. The procedure has been utilized in several wells. The original well path of the first injector was planned to maintain a safe distance above an anticipated tar-bearing zone. Utilizing the new real-time viscosity evaluation, the well was steered closer to the tar zone several feet below the original plan, setting an improved well placement protocol for subsequent injectors. In the water- or lighter hydrocarbon-bearing zones, spectral analysis of NMR logs clearly accentuated micro- and macroporous carbonate intervals. The correlation between pore size and rock quality has been corroborated by FPWD mobility measurements. In one well, an extremely slow NMR relaxation may indicate wettability alteration in a macroporous interval. An integrated real-time evaluation of porosity, fluid saturation, hydrocarbon viscosity and pore size has enhanced well placement in a heterogeneous carbonate formation where tar barriers are also present. The approach increased well performance and substantially improved reservoir understanding.

Real-Time Integrated LWD Formation Evaluation to Maximize Injectivity

2021 ◽  
Author(s):  
Idabagusgede Hermawanmanuab ◽  
Rayan Ghanim ◽  
Enrico Ferreira ◽  
Mohamed Gouda
Keyword(s):  
Real Time ◽  
Water Injection ◽  
Holistic Approach ◽  
Geological Structure ◽  
Target Zone ◽  
Horizontal Section ◽  
Well Placement ◽  
Formation Evaluation ◽  
Real Time Analysis ◽  
Resistivity Inversion

Abstract The main objective was to drill a power water horizontal injector within the sweet spot of a thin fractured and heterogeneous reservoir to achieve pressure stabilization in this producing field and an optimized sweep at the bottom of reservoir to maximize and prolong production. A traditional triple-combo logging while drilling (LWD) portfolio cannot fulfill these challenging reservoir navigation and formation evaluation (FE) objectives simultaneously because of the limited number of measurements. Hence, a more holistic approach is required to optimize the well placement via the integration of real-time LWD FE measurements to maximize the injectivity. An integrated LWD assembly was utilized and offset well FE data were studied to select the best zone for well placement to provide the best injectivity and production of the remaining oil towards the base of the reservoir. Extensive pre-well modeling was performed, based on offset well data with multiple scenarios reviewed to cover all eventualities. Another challenge was to place the wellbore in a relatively low resistive zone (water wet) in contrast to normal development wells where the wellbore is navigated in high resistive hydrocarbon bearing zones, so conventional distance to bed boundary mapping methodology was not applicable. To overcome this challenge; advanced Multi Component (MC) While Drilling resistivity inversion was proposed in conjunction with deep azimuthal resistivity technology. The benefit of this technique is in providing the resistivity of each layer within the depth of detection along with thickness and dip of each layer. Resistivity inversion results were correlated with nuclear magnetic resonance (NMR) porosity and volumetric data to identify the best zone for well placement. As MC inversion was able to map multiple layers within ~7 ft radius depth of detection, changing thicknesses and dip of each layer; the geosteering team was able to make proactive recommendations based on the inversion results. These proactive trajectory adjustments resulted in maintaining the wellbore within a thin target zone (1-3 ft in thickness) also confirmed by NMR and Formation Testing Service (FTS) in real-time, achieving excellent net-to-gross, which otherwise would not have been possible. The hexa-combo LWD assembly supported optimum well placement and provided valuable information about the geological structure through the analysis of high-resolution electrical images identifying the structural events which cause compartmentalization, confirmed by FTS results. This integrated LWD approach enabled proactive well trajectory adjustments to maintain the wellbore within the optimum porous, permeable and fractured target zone. This integrated methodology improved the contact within the water-injection target of the horizontal section, in a challenging thin reservoir and achieved 97.5 % exposure. Using an integrated LWD hexa-combo BHA and full real-time analysis the objective was achieved in one run with zero Non-Productive Time (NPT) and without any real-time or memory data quality issues.

Remote Operations: The New Norm

2021 ◽  
Author(s):  
Rachel Gajanan Kakade ◽  
Pawandeep Singh Bagga
Keyword(s):  
Real Time ◽  
Swot Analysis ◽  
Oil And Gas ◽  
Resistance To Change ◽  
Cost Optimization ◽  
Oil And Gas Industry ◽  
Global Scale ◽  
Service Companies ◽  
The World ◽  
Remote Operations

Abstract In recent years, we have seen some refined drilling technologies crop up all over the world. These have given rise to implementation of remote centers to work on real time decision making with the wells. While drilling is in process, there are technologies that enable real time transmission of data and voice to and from remote sites, helping in real time intelligent commands and responses. It is hence now possible to form a single team of experts to monitor and control drilling operations. The development of remote operations in the oil and gas industry has evolved over years starting 2004 at different speeds in different regions of the world. For example, it took longer to reach the US land market because of resistance to change at the rig site. The decrease in oil prices in 2014 however, pushed remote operations into existence to reduce cost. Due to challenges such as either oilfield culture, company strategy, human factor, legal factor etc., it was not exactly the "norm". Fast forward to 2020 when the Covid-19 pandemic hit the oil industry into another slump, service companies have been pushed into the remote operations world. To learn with the times, this may be the new norm and maybe an excellent one. Many service companies have successfully performed operations wells globally increasing not only the efficiency of wellsite operations but also contributing to cost optimization and safety. During implementation, it is observed that remote operations are less a technical challenge, and more a value challenge requiring confidence from all stakeholders. In terms of drilling and operational efficiency, the results observed globally are significant, with fewer trips for M/LWD failure, as well as significant reductions in M/LWD NPT while drilling. This paper discusses the implementation of remote operations at global scale, lesson learnt on day-to-day basis, optimization opportunities, business workflow, positives such as business continuity, safety aspect and last but not the least, the environmental impact. The paper also talks of changes and effects of Covid-19 Pandemic on these operations. Remote operations prepare us well for such pandemic and it may be the safer way to operate now on. Also discussed are the keys to successful remote operations and various examples of remote operations establishments throughout the globe. Lastly a SWOT analysis is done to conclude how remote operations will help operators to add more value to operations and show that remote operations is the new future.

Sign in / Sign up

Export Citation Format

Share Document