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Reservoir navigation, often referred to as geosteering, is commonly used to optimize the placement of highly deviated wells. This technology has contributed significantly to the prolongation and economic well-being of mature hydrocarbon provinces around the world and been a major enabler for the commercial success of unconventional reservoirs.
The reservoir information and analysis obtained during reservoir navigation is extensive and very valuable, yet is known to sometimes remain unused. Part of the reason is the complexity of reservoir navigation data and the limitations that many geosteering software applications cannot integrate the information provided from this data to update seismic-based 3D models.
This paper demonstrates a fast and effective method of utilizing spatial reservoir navigation information to improve the three-dimensional understanding of producing reservoirs. Reservoir navigation interpretations from one or more wells can be used as inputs. The results include updated structure maps, refined gross rock volume (eg shale volume in unconventional reservoirs), updated values for porosity and water saturation and ultimately a revised volumetrics calculation. The results can be compared with calculations from other methods such as material balance and decline curves. Analyzing conflicting field data and reconciling them creates opportunities for improved drilling opportunities and better reservoir development. Datasets used in the paper show some specific examples of how the 3D workflows lead to better field developments with enhanced drilling operations and improving recovery factors.
In the future, significant technical developments are expected in the type and complexity of reservoir navigation data originating from logging while drilling (LWD) tools. These data types will easily be included in the new 3D workflows without introducing undue complexity.
Integrating reservoir navigation interpretations into sub-surface 3D models can be of benefit for real time drilling operations and also for field studies. The method uses a 3D workflow that can be completed easily and is fast enough to update models in real time. It is therefore useful for the purposes of improving architectural and geomorphological understanding of an area larger in scale than just the immediate active well. This creates an information rich environment with insightful information during geosteering real time jobs for better decisions. Additionally, the analysis method can be performed as a field study. This more comprehensive approach allows integration with other information after drilling operations have ceased to improve resource recovery and pick better future drilling targets.
Maus, Stefan (H&P Technologies) | Gee, Timothy (H&P Technologies) | Mitkus, Alexander M. (H&P Technologies) | McCarthy, Kenneth (H&P Technologies) | Charney, Eric (H&P Technologies) | Ferro, Aida (H&P Technologies) | Liu, Qianlong (H&P Technologies) | Lightfoot, Jackson (H&P Technologies) | Reynerson, Paul (H&P Technologies) | Velozzi, David M. (H&P Technologies) | Mottahedeh, Rocky (United Oil & Gas Consulting Ltd)
Development of autonomous drilling technologies requires the automated analysis and interpretation of Logging While Drilling (LWD) data to optimally land the well in the target formation and keep it in the pay zone. This paper presents a fully automated geosteering algorithm, which includes advanced LWD filtering, fault detection, correlation, tracking of multiple interpretations with associated probabilities and visualization using novel stratigraphic misfit heatmaps.
Traditional geosteering uses manual stretch, compress and match techniques to correlate measurements along the subject wellbore against corresponding reference type logs. This results in a crude representation of strata by linear sections with offsets at fault locations. Instead of automating this manual process, we instead determine the possible interpretations as solutions of a geophysical inverse problem in which the total misfit between the subject and reference data is minimized. Interpretations are parameterized as discontinuous splines to accurately follow curved strata interjected by fault offsets. To account for ambiguities, multiple possible interpretations are continuously tracked in real time and assigned probabilities based on the misfit between the latest measurements and the reference data. Unrealistic solutions are suppressed by penalizing strong curvature and large fault offsets. Viable interpretations are simultaneously visualized in real time as paths on a novel stratigraphic misfit heat map, where they may be corroborated against valleys of minimal misfit between the subject and reference data. The user can guide the interpretation by setting control points on the heat map which the automated solutions must respect.
The algorithm has been validated using wells from different regions across North America for which previous manual geosteering interpretations are available. The automated spline interpretations represent the actual curved strata more accurately than manual interpretations. Operationally, the automated interpretations can be provided within minutes compared to typical manual turn-around times of hours. Automation leads to more consistent and repeatable results, removing the subjectivity of manual interpretations.
We propose a Bayesian estimator for real-time autonomous geosteering. The Bayesian geosteering tool is capable of simultaneously estimating the stratigraphic variables and tool location. We use gamma-ray well-log measurements to perform the estimation. Given the prior information and measurements, the Bayesian estimator can rigorously compute the joint posterior probability density function of the stratigraphic and tool-location variables. Due to the inherent nonlinearity of measurements and the non-Gaussianity of the random variables involved, we propose a sequential Monte Carlo filter for performing the inference. Unlike the widely used Kalman filter and its variants, the estimation performance of sequential Monte Carlo estimator is not constrained by the nature of dynamics, measurement functions and the type of uncertainties. The computational cost of the estimation is kept manageable by making a few simplifying assumptions. The estimation performance of the proposed sequential Monte Carlo based geosteering tool is demonstrated with a simulated example involving six formation tops. The performance is evaluated in terms of the ability of the estimator to accurately track the stratigraphic boundaries and predict the correct formations. The results show that the proposed Bayesian geosteering tool can predict the stratigraphic boundaries and the type of formation in which the tool is located in a probabilistically rigorous fashion.
Understanding reservoir fluid and facies distribution is crucial for optimal reservoir development. Ultra-deep, logging-while-drilling (LWD) resistivity measurements with a deep detection range into the formation have started a new chapter of formation evaluation. A hybrid inversion of statistical and deterministic approaches based on ultra-deep measurements has successfully determined formation resistivity profiles more than 100 ft away from drilled wellbores, providing proactive geosteering information for real-time well-placement decisions. However, the inversion sometimes produces artificial geological features because of so-called solution ambiguities attributable to lower measurement sensitivity in certain formation resistivity contrasts and reservoir geometries. Previously, geosteering geologists were trained to recognize such unrealistic geological structures based on multiple sources of information, rather than just the ultra-deep resistivity inversion results.
This paper introduces machine-learning (ML) algorithms to evaluate the sensitivity of individual measurements, as well as to cluster the inverted models to acquire more geologically reasonable models of the surrounding formations. A case study shows significant improvement as a result of the ML algorithm in the structural consistency of the reservoirs. The boundaries were better determined with fine details using the ML algorithm, as compared to results from existing algorithms. The enhanced answer product enabled a better understanding of the formation properties surrounding the wellbore and retrieved several fine features that were not observed previously.
Understanding reservoir fluid and facies distribution is crucial for optimal reservoir development. Ultradeep, logging-while-drilling (LWD) resistivity measurements with a deep detection range into the formation have started a new chapter of formation evaluation. A hybrid inversion of statistical and deterministic approaches based on ultra-deep measurements has successfully determined formation resistivity profiles more than 100 ft away from drilled wellbores, providing proactive geosteering information for realtime well-placement decisions. However, the inversion sometimes produces artificial geological features because of so-called solution ambiguities attributable to lower measurement sensitivity in certain formation resistivity contrasts and reservoir geometries. Previously, geosteering geologists were trained to recognize such unrealistic geological structures based on multiple sources of information, rather than just the ultradeep resistivity inversion results. This paper introduces machine-learning (ML) algorithms to evaluate the sensitivity of individual measurements, as well as to cluster the inverted models to acquire more geologically reasonable models of the surrounding formations. A case study shows significant improvement as a result of the ML algorithm in the structural consistency of the reservoirs. The boundaries were better determined with fine details using the ML algorithm, as compared to results from existing algorithms. The enhanced answer product enabled a better understanding of the formation properties surrounding the wellbore and retrieved several fine features that were not observed previously.
Imomoh, Victor (Baker Hughes, a GE company) | Ndokwu, Chidi (Baker Hughes, a GE company) | Amadi, Kenneth (Baker Hughes, a GE company) | Toyobo, Oluwaseun (Baker Hughes, a GE company) | Nwabueze, Ikechukwu (Baker Hughes, a GE company) | Okowi, Victor (Baker Hughes, a GE company) | Ajao, Oyekunle (Chevron Nigeria Limited.) | Okeke, Genevieve (Chevron Nigeria Limited.) | Dada, Yemi (Chevron Nigeria Limited.) | Jumbo, Sandison (Chevron Nigeria Limited.) | Aina, Soji (Chevron Nigeria Limited.)
Oil and gas drilling has fully embraced the practice of drilling horizontal and extended-reach wells in place of deviated wells to avoid multi-platform drilling and increase hydrocarbon recovery. However, the producer is still faced with multiple challenges that include lateral facies change, lateral variation in reservoir properties and structural uncertainties. Consequently, it is paramount that continuous advancement is achieved in combining fit-for-purpose, real-time logging-while-drilling (LWD) solutions to assist further in the enhancement of hydrocarbon recovery.
Reservoir navigation services (RNS) involve predicting the geology ahead of the bit to place the wellbore correctly in the zone of interest in a horizontal or near-horizontal path. LWD data, obtained from downhole drilling suites, transmitted in real time through mud pulses to a surface computer where the data are interpreted and used to steer the well in the desired direction. Formation pressure while drilling (FPWD) is a process of acquiring reservoir pressures downhole and this is done with a specialized downhole LWD pressure-testing tool. The use of RNS in Well-MX played a significant role in the drilling project – landing Well-MX in the targeted M reservoir bed and drilling the lateral section. The major geosteering technologies used are the at-bit resistivity and azimuthal propagation resistivity, which provides geostopping capability, reservoir bed boundary mapping and accurate distance to bed boundary calculation. These technologies helped in keeping the wellbore within the hydrocarborn unit of the M reservoir. Performing formation pressure testing in realtime, the team was able to carry out a reservoir gradient analysis which helped with reservoir fluid identification, fluid contact determination, and connectivity of hydrocarbon zones before drilling was concluded.
Well-MX is a horizontal well located in the Mirum field of the Niger Delta Basin, offshore Nigeria. The well was drilled to target the deep multi-lobed M reservoir to a total hole depth of 11,307ft MD. By using Well-MX as a case study, this paper discusses how the combination of reservoir navigation service and real-time formation pressure sampling helped meet drilling objectives for this well. Some of the challenges encountered includes vertical seismic interpretation uncertainty, poor reservoir quality along the drain hole section, change in depth of oil to water contact and undulating bed boundaries. Other challenges and decisions taken to successfully geosteer the well will be reviewed in this paper.
Ndokwu, C. (Baker Hughes, a GE company.) | Amadi, K. (Baker Hughes, a GE company.) | Toyobo, O. (Baker Hughes, a GE company.) | Okowi, V. (Baker Hughes, a GE company.) | Ajisafe, I. (Addax Petroleum Development Nigeria Ltd.) | Inenemo, A. (Addax Petroleum Development Nigeria Ltd.)
Due to the low oil price, Exploration and Production (E&P) companies are driven to reduce the cost per barrel of oil equivalent (BOE). The application of reservoir navigation services, in the placement of high angle and horizontal (HAHZ) wells in the sweet spot of reservoirs, has aided in meeting this economic need of the E&P, while also improving hydrocarbon recovery. Reservoir navigation services (RNS) can be regarded as another tool for improving the odds of success while drilling of HAHZ wells. This service involves the integration of real-time data (deep-reading azimuthal resistivity, gamma-ray, density image, resistivity image logs, near bit inclination and a fit for purpose rotary steerable system) to accurately position the well-bore relative to specific subsurface targets, while remaining within the constraints of the drilling and completion program. RNS also require a software package capable of pre-well modeling, displaying the acquired real-time data and interactively adapting the model to the real-time data. Geosteering in Njaba field involved a comprehensive pre-well planning, discussions, documentation and management approved decision-tree. Using three wells for this study, this paper describes the challenges, procedures and results of geosteering in Njaba Field located onshore Niger-Delta. From different entry points, wells NJX1, NJX2, and NJX3 were planned to drain the same reservoir and optimize hydrocarbon recovery within the reservoir. Some of the challenges encountered includes geosteering the wellbore above a predetermined production TVD hardline while simultaneously avoiding drilling into an overlying undulating shale cap rock, vertical seismic uncertainty and undulating formation boundaries.
Geosteering has relied on manual log interpretation for decades. This paper outlines a new, patent-pending method of automated geosteering dubbed Cybersteering. By utilizing a graph database spatially and assessing the quality of gamma matching for a large catalog of potential segments of constant bed dip (strat blocks), a geosteer can in many cases be constructed that closely mimics that of a manual steer. The Cybersteering proof of concept has the ability to lighten geosteering workloads while increasing productivity and accuracy of geosteers.
Geosteering as a system and method for controlling a wellbore based on downhole geological measurements to stay within a pay zone originally rose to prominence in US onshore drilling in the late 1980s and early 1990s (Lesso, Jr.). Wells came to be steered with gamma ray once logging-while-drilling and measurement-while-drilling tools became more common, although steering based off of rate of penetration and mud samples was also common. Innovations since then have included the utilization of resistivity logs, various uses of seismic data, as well as experimentation with technologies such as mass spectrometry and x-ray diffraction (Durham). However, there has yet to be as large an advancement in geosteering as the initial move from the tedious analysis of paper logs to software that can display data graphically. Within such software, geosteerers can stretch or squeeze gamma ray log sections to match a total vertical depth (TVD) type log from a nearby well in order to correlate a well's stratigraphic depth. The general process requires a geosteerer to review gamma and trajectory data every time a survey comes in and visually determine the best overall gamma match between wellbore and type log through the manipulation of strat blocks, which are sections of constant bed dip. The gamma match is changed and determined by manually varying strat block length and angles (Stoner). A typical geosteering screenshot is shown in figure 1.
Zhu, Meng (CNOOC China Ltd. Tianjin) | Cui, Yunjiang (CNOOC China Ltd. Tianjin) | Ma, Chao (CNOOC China Ltd. Tianjin) | Xu, Jinxiu (CNOOC China Ltd. Tianjin) | Yang, Wei (CNOOC China Ltd. Tianjin) | Li, Ting (Schlumberger)
Caofeidian oilfield in Bohai Sea has the characteristics of low structural amplitude, low oil column height, complex structure and reservoir change, complex fluid system, large reserve of bottom water reservoir, and all use horizontal well development. How to accurately control the well trajectory to make the horizontal well successfully landing, improve the level of drilling encounter rate, to achieve the ideal height of water protection is the key to achieve the optimal development effect. After many years of development, the geological-oriented technology has been adopted for the drilling of the oil field, which has achieved good application results. However, with the development of the oil field, the reservoir changes facing at present are more complex, and the reservoir with lower oil column height, which is limited by the density of the offshore well net, the traditional Geological guidance technology of follow-drill logging has great limitations, and it satisfies the implementation requirements of horizontal well.
Aiming at the above problems, the seismic attributes are connected with the logging curve through the cluster analysis method, and the natural gamma and over-well seismic attributes (maximum amplitude) are innovated) the relationship between the resistivity and the natural gamma curve is established, and the pre-drilling geological guidance model is established.
This method not only gives the meaning of seismic attribute logging, it also solves the shortcomings of the traditional modeling error caused by the large horizontal difference between the design wells and the guide wells, the difference of fluid properties and the small detection of the fan enclosure. At the same time, the geological guidance technology in Caofeidian Oil Field low oil column bottom water reservoir with Drilling Technology series, improve the landing prediction accuracy and level segment well trajectory control ability. In the past three years, nearly 50 horizontal wells were implemented in Caofeidian Oilfield, and the average oil layer drilling rate of the horizontal segment reached 90, according to statistics, it has improved 10% on the drilling encounter rate before using the geological guidance technology, which is also adaptive in other oil fields, and 4 horizontal wells were drilled in the B oilfield in 2018, the gamma-ray and resistivity curves of non-drilled sand bodies above the target layer have been successfully retrieved, and the aim of accurately guiding the horizontal well landing has been achieved.
Geosteering services could improve geological reservoir development effect, decrease difficulty in drilling and completion engineering, reduce drilling cycle, and enhance economic performance of oil & gas field development. The authors believe that scientific and effective development of oil & gas fields requires all departments and professions to cooperate closely, and the cooperation could get more perfect by timely using geosteering services. Geosteering services play an important role in reservoir engineering, drilling and completion, and drilling management, etc., connecting various professions seamlessly.
Baker Hughes drilled one horizontal well for major Indian operating company in a, low resistivity contrast field, onshore India. The candidate field / basin is a proved petroliferous basin, located in the northeastern corner of India. The scope of work for this project involved integrating geological and open hole offset parameters to build a Geosteering model. Integrated data included a study of offset well data from the field, regional and local dip analysis from wellbore images, and a review of structural maps. Successful integration of these data helped to steer the well in the desired zone as per plan and make the best use of the data and to reduce uncertainties in Geosteering, drilling. Although high-quality 16-sector images commonly yield bedding dip, fracture and other geological information, this paper emphasizes how real-time reservoir navigation decisions was made using Geosteering modelling, real-time image processing, dip picking study etc.