The acquisition of downhole pressure data representative of reservoir response enabling subsequent pressure transient analysis has been one of the primary drivers for running drill stem tests. However, many factors can influence the representativity and interpretability of the data acquired that are not related to reservoir properties.
To our knowledge, while many publications have presented challenges in acquiring representative pressure data those have not been compiled in a comprehensive revies, and there are no practical recommendations that would summarise causes and effects and offer procedures to eliminate or at least manage those effects and enable end-users to maximize the value of acquired data.
This paper describes in details today's challenges associated with the acquisition of high-quality, representative and undisturbed bottom hole pressure data during well test operations. Many different effects, including gauges’ deployment methods, wellbore effects and operational aspects of the test can compromise the quality of bottom hole data acquired while running a welltest.
Therefore, the origin and impact of each of these effects needs to be evaluated at the design stage of the test to develop appropriate mitigation actions. To address these issues, actual examples and methodologies derived from various locations are presented.
Over the years the metrological performances of downhole memory gauges such as resolution or drift have improved drastically, reaching a point where gauge specifications have become less influential on data quality than environmental effects. Many improvements have also been made in DST tools to increase the representativity and interpretability of acquired bottom hole pressure data such as the introduction of downhole shut-in valves or compensation for tubing contraction and expansion due to temperature change during the test. However, there remain several occurrences today where memory gauge data are affected by the various wellbore phenomena making interpretation of downhole pressure transient test data complicated. The selection of an appropriate location of pressure sensors in the DST string also remains a crucial task.
The paper provides analysis, explanations and practical recommendations allowing to mitigate the most common effects typically observed during welltest operations performed around the world, such as: Tidal effect Fluid segregation effect in the wellbore Pressure noise propagation from the surface due to rig movement The impact of application of electrical submersible pump (ESP) on the quality of pressure build-up data "Hammer effects" during well shut-in Impact of circulation above the test valve during PBU Impact of pressure bleed off and top up in the annulus Fluid cooling effect in the wellbore Gauge movement due to string contraction and expansion
Fluid segregation effect in the wellbore
Pressure noise propagation from the surface due to rig movement
The impact of application of electrical submersible pump (ESP) on the quality of pressure build-up data
"Hammer effects" during well shut-in
Impact of circulation above the test valve during PBU
Impact of pressure bleed off and top up in the annulus
Fluid cooling effect in the wellbore
Gauge movement due to string contraction and expansion
This paper will summarise the observation and lessons learned from hundreds of welltest operations performed around the globe with different reservoir fluids and environments through a few telling examples. Furthermore, the paper provides practically proven well-test techniques allowing to manage those adverse effects on bottom-hole pressure data. Recipes for success are provided to ensure that high-quality data can be acquired during welltest operations in a challenging environment while keeping the cost in line with the AFEs.
With the recent tremendous development in algorithms, computations power and availability of the enormous amount of data, the implementation of machine learning approach has spurred the interest in oil and gas industry and brings the data science and analytics into the forefront of our future energy. The idea of using automated algorithms to determine the rock facies is not new. However, the recent advancement in machine learning methods encourages to further research and revisit the supervised classification tasks, discuss the methodological limits and further improve machine learning approach and classification algorithms in rock facies classification from well-logging measurements. This paper demonstrates training different machine learning algorithms to classify and predict the geological facies using well logs data. Previous and recent research was done using supervised learning to predict the geological facies.
This paper compares the results from the supervised learning algorithms, unsupervised learning algorithms as well as a neural network machine learning algorithm. We further propose an integrated approach to dataset processing and feature selection. The well logs data used in this paper are for wells in the Anadarko Basin, Kansas. The dataset is divided into training, testing and evaluating wells used for testing the model. The objective is to evaluate the algorithms and limitations of each algorithm. We speculate that a simple supervised learning algorithm can yield score higher than neural network algorithm depending on the model parameter selected. Analysis for the parameter selection was done for all the models, and the optimum parameter was used for the corresponding classifier.
Our proposed neural network algorithm results score slightly higher than the supervised learning classifiers when evaluated with the cross-validation test data. It is concluded that it is important to calculate the accuracy within the adjacent layers as there are no definite boundaries between the layers. Our results indicate that calculating the accuracy of prediction with taking account the adjacent layers, yield higher accuracy than calculating accuracy within each point. The proposed feed-forward neural network classifier trains using backpropagation (gradient descent) provides accuracy within adjacent layers of 88%. Our integrated approach of data processing along with the neural network classifier provides more satisfactory results for the classification and prediction problem. Our finding indicates that utilizing simple supervised learning with an optimum model parameter yield comparable scores as a complex neural network classifier.
Al-Maqtari, Ameen N. (SAFER E&D Operations Company) | Saleh, Ahmed A. (SAFER E&D Operations Company) | Al-Haygana, Adel (SAFER E&D Operations Company) | Al-Adashi, Jaber (SAFER E&D Operations Company) | Alogily, Abdulkhalek (SAFER E&D Operations Company) | Warren, Cassandra (Schlumberger) | Mavridou, Evangelia (Schlumberger) | Schoellkopf, Noelle (Schlumberger) | Sheyh Husein, Sami (Schlumberger) | Ahmad, Ammar (Schlumberger) | Baig, Zeeshan (Schlumberger) | Teumahji, Nimuno Achu (Schlumberger) | Thiakalingam, Surenthar (Schlumberger) | Khan, Waqar (Schlumberger) | Masurek, Nicole (Schlumberger) | Andres Sanchez Torres, Carlos (Schlumberger)
A 3D petroleum systems model (PSM) of Block 18 in the Sab'atayn basin, onshore western Yemen, was constructed to evaluate the untapped oil and gas potential of the Upper Jurassic Madbi formation. 3D PSM techniques were used to analyze petroleum generation for conventional reservoirs and the petroleum saturations retained in the source rock for the unconventional system. Block 18 has several proven petroleum systems and producing oil and gas fields. The principal source rocks are within the Madbi Formation, which comprises two units, the Lam and the Meem members. Both contain transgressive organically rich "hot" shales with total organic carbon (TOC) of 8 to 10%; these are located stratigraphically at the base of each member. Additional organic-rich intervals within the Lam and Meem are less-effective source rocks, with lower TOC values.
The PSM consisted of 17 depositional events and 2 hiatuses. To accurately replicate geochemical and stratigraphic variations, the Lam and Meem members were further divided into sublayers. The model was calibrated to present-day porosity, permeability, and pressure data, and it incorporated vertical and lateral lithofacies and organic facies variations. Further calibrations used observed maturities (vitrinite reflectance and pyrolysis Tmax) and present-day temperatures and considered laterally variable heat flow from the Early Jurassic to the Late Miocene. Finally, petrophysical analyses from wells provided calculated hydrocarbon saturations, which were used to calibrate the saturation output from the model. The model satisfactorily reproduces the distribution of the main gas and oil fields and discoveries in the study area and is aligned with well test data.
Maturity results indicate that the upper Lam intervals currently sit within the main to early oil window but are immature at the edges of Block 18 (based on the Sweeney and Burnham Easy R0% kinetics). The lowest Lam unit enters the wet gas window in the center of the block. The underlying Meem member ranges from wet gas to early oil window maturity. Like the Lam, the Meem remains immature along the edges of Block 18. However, in the south of the block, the richest source rocks within the Meem are mainly in the oil window. The degree of transformation of the Meem and Lam varies throughout the members. The model predicts that, at present, the lowest part of the Meem, containing the greatest TOC, has 90% of its kerogen transformed into hydrocarbons.
The model confirms that the Madbi formation is a promising unconventional shale reservoir with a high quantity of hydrocarbons retained within it. Despite the higher quantity of hydrocarbons retained in the upper Meem, in terms of liquid and vapor hydrocarbons predicted in this model, the lower Lam is the most-prospective conventional tight sand reservoir, and the Meem has very small potential as tight sand reservoirs. This study provided a novel application of 3D PSM technology to assess new unconventional as well as conventional plays in this frontier area.
Drilling hazards can lead to significant cost overruns during the drilling phase and might cause unsafe situations or potentially harm the environment. Often the local geology, when poorly understood, is the trigger of a drilling incident. By sharing past drilling experience and in particular observations on Geo-Drilling Hazards, via a suitable platform, well planning and risk assessment can be carried out more effectively. After analysing historic drilling reports, observations on drilling incidents have been compiled using a structured approach. Classification schemes allow systematic capture of key information in a format suitable for a database. In this process the observations (
The Geo-Drilling Events (GDE) database currently covers some 1000 boreholes from the Netherlands. Around 1400 geo-drilling events have been analysed systematically allowing to identify drilling hazard hotspots in a statistically meaningful sense. Examples of geo-drilling events include
Planned well trajectories can now be screened efficiently for geo-drilling hazards. The GDE Tool based on advanced classification criteria allows to share relevant well information across all operators active in the Netherlands. This includes newcomers, like geothermal operators who carry out a lot of drilling nowadays. The GDE Tool allows everyone to learn from the experience on drilling hazards gathered over the years by oil companies.
The Young Member Outstanding Service Award recognizes contributions to and leadership in the public and community areas, as well as SPE, the profession, and the industry, by a member under age 36. Four awards were presented at the 2012 SPE Annual Technical Conference and Exhibition Annual Reception and Banquet, held 9 October in San Antonio, Texas. Do you think this award will have an impact on your professional life? This award to me is in recognition of my effort on behalf of and dedication toward SPE. An acknowledgment like this at such a high level provides the impetus and encouragement for me to carry on.
Weijermans, Peter-Jan (Neptune Energy Netherlands B.V.) | Huibregtse, Paul (Tellures Consult) | Arts, Rob (Neptune Energy Netherlands B.V.) | Benedictus, Tjirk (Neptune Energy Netherlands B.V.) | De Jong, Mat (Neptune Energy Netherlands B.V.) | Hazebelt, Wouter (Neptune Energy Netherlands B.V.) | Vernain-Perriot, Veronique (Neptune Energy Netherlands B.V.) | Van der Most, Michiel (Neptune Energy Netherlands B.V.)
The E17a-A gas field, located offshore The Netherlands in the Southern North Sea, started production in 2009 from Upper Carboniferous sandstones, initially from three wells. Since early production history of the field, the p/z plot extrapolation has consistently shown an apparent Gas Initially In Place (GIIP) which was more than 50% higher than the volumetric GIIP mapped. The origin of the pressure support (e.g. aquifer support, much higher GIIP than mapped) and overall behavior of the field were poorly understood.
An integrated modeling study was carried out to better understand the dynamics of this complex field, evaluate infill potential and optimize recovery. An initial history matching attempt with a simulation model based on a legacy static model highlighted the limitations of existing interpretations in terms of in-place volumes and connectivity. The structural interpretation of the field was revisited and a novel facies modeling methodology was developed. 3D training images, constructed from reservoir analogue and outcrop data integrated with deterministic reservoir body mapping, allowed successful application of Multi Point Statistics techniques to generate plausible reservoir body geometry, dimensions and connectivity.
Following a series of static-dynamic iterations, a satisfying history match was achieved which matches observed reservoir pressure data, flowing wellhead pressure data, water influx trends in the wells and RFT pressure profiles of two more recent production wells. The new facies modeling methodology, using outcrop analogue data as deterministic input, and a revised seismic interpretation were key improvements to the static model. Apart from resolving the magnitude of GIIP and aquifer pressure support, the reservoir characterization and simulation study provided valuable insights into the overall dynamics of the field – e.g. crossflows between compartments, water encroachment patterns and vertical communication. Based on the model a promising infill target was identified at an up-dip location in the west of the field which looked favorable in terms of increasing production and optimizing recovery. At the time of writing, the new well has just been drilled. Preliminary logging results of the well will be briefly discussed and compared to pre-drill predictions based on the results of the integrated reservoir characterization and simulation study.
The new facies modeling methodology presented is in principle applicable to a number of Carboniferous gas fields in the Southern North Sea. Application of this method can lead to improved understanding and optimized recovery. In addition, this case study demonstrates how truly integrated reservoir characterization and simulation can lead to a revision of an existing view of a field, improve understanding and unlock hidden potential.
The Vega subsea field in Norway has been producing successfully using a continuous Mono Ethylene Glycol (MEG) injection, topped up with corrosion inhibition means. A topside reclamation process allows reuse of MEG, however, limits the possibilities to produce saline water. In order to manage wells producing saline formation water and to increase ultimate recovery, a new flow assurance and integrity philosophy without continuous MEG injection is considered. This paper describes the options on hydrate as well as integrity management and the modifications both on the subsea and topside facilities required to enable an operational philosophy change. This change of the operational philosophy appears feasible, using either timely depressurization or Low Dosage Hydrate Inhibitors (LDHI) as well as a film building corrosion inhibitor in the system.
A long-term suspended subsea exploration well within a producing gas reservoir needed to be decommissioned after 21 years. During a pre-decommissioning diving campaign, bubbles confirmed as reservoir gas were observed to be percolating from the well bore through a hard silt / cement debris plug inside the wellhead. A pressure study established that the reservoir may have re-charged to 2,200 psi. An alternative pressure controlled well re-entry method was required to safely re-enter, tie-back the well to surface with 16-in. high pressure riser, install BOP while preventing gas from reaching the rig floor from seabed. Two existing cement plugs would then be drilled out under controlled conditions due to the potential for high-pressure gas beneath the plugs. Casing integrity evaluation and cement bond logging would be carried out to establish the path of gas ingress into the wellbore. Remedial work would be conducted, and permanent abandonment barriers installed in the well. Casings and wellheads would then be recovered from a depth below the seabed.
A customized managed pressure drilling (MPD) system was designed using a rotating control device (RCD) and modified drilling chokes. A pioneering plan was developed to meet the specific well re-entry requirements of the percolating suspended well to account for the potential for virgin reservoir pressure at seabed and the wellhead silt plug preventing deployment of BOP test tools. A hazard and operability study (HAZOP) was conducted with key personnel, which supported development of well-specific operating procedures and decision matrices. Successful deployment included MPD system calibration, well behavior fingerprinting, and training of rig personnel at the well site.
The combination of experienced personnel, innovative MPD equipment, specific procedures, team interactions and risk analyses were key to safely completing this well re-entry and decommissioning scope. The strategy enabled drilling out of two cement plugs with potential high-pressure gas trapped beneath them. Both cement plugs, 356ft and 669ft long, were drilled without any well-control or plugged-choke events. Throughout the process, the well was monitored using MPD equipment, which included an RCD on top of rig's BOP, modular drilling chokes and multiple pressure gauges and sensors installed at critical points. Additionally, temporary modifications were made to the rig and new lines of communication between the rig crew and the MPD team were established to ensure all pressures were correctly interpreted and the decision matrix was correctly applied. An effective close partnership developed between the equipment service provider, well operator and drilling contractor was a key enabler to deliver this very challenging novel implementation of MPD technology within eight weeks. The MPD approach was estimated to have saved 9 days of rig time, when compared to alternative coiled tubing-based solutions.
This paper describes the first MPD-assisted well re-entry for well decommissioning in the UK North Sea sector. The novel application of existing technology can help operators to cost effectively re-enter and decommission troublesome legacy wells without harm to people, environment or assets. This new approach resulted in the safe unconventional re-entry and decommissioning of a potentially live gas well.
This paper presents a Digital Twin concept aimed at assets in the oil & gas and wind industry, that provides an accurate estimate of the true fatigue life of these assets in order to unlock potential fatigue life and ultimately extend the life of assets. This concept is divided in four tiers that allow to unlock remaining fatigue life one after the other. The first tier consists of using a high-resolution finite element model of the asset, delivered by Akselos unique RB-FEA technology. The subsequent tiers consist in using data from a few strategically placed accelerometers, as well as wave radar recordings, in order to calibrate the model and estimate the real loading on the asset. This concept delivers a true digital twin of the asset and offers a compelling and costeffective method for offshore assets that are facing life time extension beyond what current methodologies can provide. The concept is being implemented on one of Shell's platforms in the Southern North Sea.
Understanding petrophysical properties well enough to make drilling decisions, particularly for tight gas can be a challenge. A new computer system aims to help analyze the extensive data involved. There are more than 100 accumulations in the southern North Sea that are flagged as stranded fields. One of these stranded tight gas fields, the Kew field, has been developed successfully with the use of a subsea well, horizontal drilling, and hydraulic fracturing.