By International Petroleum Technology Conference (IPTC) Monday, 25 March 0900-1600 hours Instructors: Olivier Dubrule and Lukas Mosser, Imperial College London Deep Learning (DL) is already bringing game-changing applications to the petroleum industry, and this is certainly the beginning of an enduring trend. Many petroleum engineers and geoscientists are interested to know more about DL but are not sure where to start. This one-day course aims to provide this introduction. The first half of the course presents the formalism of Logistic Regression, Neural Networks and Convolutional Neural Networks and some of their applications. Much of the standard terminology used in DL applications is also presented. In the afternoon, the online environment associated with DL is discussed, from Python libraries to software repositories, including useful websites and big datasets. The last part of the course is spent discussing the most promising subsurface applications of DL.
Gelinsky, Stephan (Shell International E&P) | Kho, Sze-Fong (Shell International E&P) | Espejo, Irene (Shell International E&P) | Keym, Matthias (Shell Malaysia) | Näth, Jochen (BSP) | Lehner, Beni (BSP) | Setiana, Agus (BSP) | Esquito, Bench (SDB) | Jäger, Günther (SDB)
Prospects below or near shallower producing fields can be economically attractive yet also risky since reservoir presence may be uncertain, reservoir quality can be poor, and high overpressure and temperature can make drilling and logging deeper prospects difficult. Systematic integration of relevant subsurface data from thin section to basin scale allows to seismically identify reservoir presence, and to predict reservoir quality for applicable rock types via burial histories. On an intermediate well log to seismic scale, a predictive rock physics modeling approach links reservoir and seal rock properties to seismic amplitude data to polarize the prospect's geologic ‘probability of success'. Particular challenges in the offshore Brunei study were very fine-grained deposits and non-vertical tectonic stresses associated with compressional settings. Both make porosity predictions that leverage complex burial histories rather than relying on extrapolated depth trends quite challenging - yet the integrated approach remains the best option to identify deep reservoir quality sweetspots that a favorable stress and temperature history may have preserved for certain reservoir rock types in certain locations.
The prolific petroleum system offshore Brunei features two major sediment fairways, the Baram and Champion river systems, and a variety of depositional environments, ranging from high NtG topsets inboard over shallow marine slope settings to deepwater turbidites outboard (
Over recent years, many authors have proposed to compensate the absorption loss effects inside of the imaging process through the use of an attenuation model. This is necessary in the presence of strong attenuation anomalies. Q tomography has been developed for estimating this attenuation model but is generally limited to estimating attenuation in predefined anomaly areas. In this paper, we show how shallow gas pockets are revealed automatically by using a high-resolution volumetric Q tomography on the complex offshore Brunei dataset. A key component of our approach is the estimation of effective attenuation in pre-stack migrated domain through accurate picking of the frequency peak. Estimated Q-model is then used to compensate for absorption in the imaging process.
The Brunei region is considered as a complex area known for its gas escaping features over folded structures, producing shallow strong absorption anomalies. These strong anomalies seriously mask the coherency of the structure beneath.
Typically, the overall effect on the signal is that higher frequencies are dimmed more rapidly as the signal propagates through these very attenuating media. This results in a loss of signal resolution. Conversely, the attenuated signal carries additional information that can be useful in locating such gas pockets.
Measured attenuation can be compensated by applying processes such as the early techniques of inverse-Q filtering (Wang, 2002). More recently, stronger compensation due to gas or mud was included directly in the imaging process (Xie et al., 2009; Fletcher et al., 2012) through an interval Q model computed by tomography (Xin et al., 2008; Cavalca et al., 2011; Xin et al., 2014, Gamar et al., 2015). Generally, effective Q quantities are then inverted to produce a 3D interval Q model. The main purpose of tomography is to de-noise effective Q measurements in a model-consistent manner. Because the tomographic inverse problem is poorly constrained due to a difficult estimation of effective attenuation, a priori information is introduced to guide the inversion.
We present a robust workflow that uses Q tomography for converting dense inhomogeneous prestack effective Q measurements into a 3D model-consistent interval Q. To compute the effective Q volume in the pre-stack domain, we have used the method proposed by Zhang and Ulrych (2002) based on the shift of the frequency peak. Since the frequency peak (frequency at maximum amplitude) is very sensitive to the noise, we increase the signal/noise ratio by using the autocorrelation of the signal rather than the signal itself. This improves the resolution of the frequency peak value and thus the accuracy of effective Q estimation. We apply the workflow on Brunei offshore dataset to localize shallow gas pockets without any a priori information on their positions. This was made possible thanks to an adaptation to Q tomography of non-linear slope tomography (Guillaume et al., 2011) using an accurate effective Q volume picked from pre-stack migrated gathers.
The Maharaja Lela Jamalulalam field (MLJ), located in Brunei Block B, was discovered in february1990 and has been producing oil, gas and condensates since 1999. It is operated by Total E&P Borneo B.V. (TEPB) with a 37.5% interest. The remaining interests in the Block B Joint Venture (BBJV) are held by Shell Deepwater Borneo Limited (SDBL), 35% and Petroleum Brunei ExPro (PBE), 27.5%. Figure 2 MLJ installations layout schematic and Lumut plant Block B is located approximately 50 km offshore Brunei in shallow waters (65m) and comprises the main sub-block (B4) and 2 separated smaller sub-blocks (B6 and B2) located respectively to the South and East. The main subblock contains the Maharaja Lela Jamalulalam field (MLJ) and the production facilities.
In the quest of improving the hydrocarbon recovery and reducing the development cost in the challenging multi-stacked compartmentalized fields as well as oil rim reservoirs in Malaysia, well type and completion design was found to play a major role. Intelligent well design and completions, namely multi-lateral, selective and controlled injection and depletion, dynamic active flow control valves and down-hole pressure/temperature/composition monitoring have been identified as an essential component in the enhancement of the development strategy. Smart/intelligent completions have the ability to prevent/delay water or gas breakthrough, increase the productivity index and also to properly control drawdown to mitigate wellbore instability, sand failure and conformance issue. Active flow control valves also allow for fewer wells to be drilled by enabling efficient commingled injection and production wells. Moreover, with down-hole monitoring and surveillances, unplanned and challenging work-overs can be avoided, further reducing operating costs. The study also focuses on well architecture that entails well type selection, well reservoir penetration, well inclination and orientation, well completion simplification, well placement and well-count optimization.
In this paper, examples of mature complex multi-stacked and compartmentalized reservoirs with very thin to thick oil columns have been studied for improving the development and exploitation strategies through application of intelligent well type and optimum completion design and engineering. The suggested technology tool box including the applied workflow, guideline, procedures and standards with the field examples and desired results are to be presented and discussed. The study will cover from the assessment to the implementation and execution as well as the modeling methodology of the smart well technology on the selected fields in Malaysia.
Malaysia features a plethora of reservoirs that are; multi-stacked, compartmentalized and/or marginal oil rims (wedged in-between a gas-cap and an aquifer). Additionally these reservoirs are complex in structure with relatively high levels of reservoir heterogeneity. By their nature, these reservoirs present a challenge to be commercially productive as well as viable for active reservoir management.
An intelligent well design and completion feasibility study was commissioned to critically analyze the technical and commercial impact of application of this technology to specific fields in Malaysia. A pilot selection of 4 fields with reservoir characteristics representative of Malaysia's wide range of diversified oil and gas fields were chosen as a basis for this case study. The purpose of the study was to help qualify and quantify the field specific benefits of incorporating intelligent completions as part of field development plans. The results of this study would form part of an inventory resource to be utilized for realizing a field's optimal value via efficient development of mature, complex, multi-stacked, compartmentalized and thin-oil rim reservoirs.
Bakker, Peter (Brunei Shell Petr. Sdn Bhd) | Liew, Yee Yung (Brunei Shell Petr. Sdn Bhd) | Dale-Pine, Brigitte (Brunei Shell Petr. Sdn Bhd) | Watts, Lee (Brunei Shell Petr. Sdn Bhd) | Salakhetdinov, Ravil (Brunei Shell Petr. Sdn Bhd)
The economic development of thin oil rims associated with large gas caps in multi-stacked reservoirs presents many challenges. In the structurally complex Champion West field, offshore Brunei, innovative well designs together with detailed subsurface studies have unlocked thin oil rims. Earlier development phases comprised deviated and snake wells with multi-zone smart completions. In some areas these wells have resulted in cross-fault depletion of the remaining smaller oil targets. An appraisal strategy was put in place to address the major uncertainties associated with the smaller targets, such as complex fluid distribution and connectivity to existing producers. This strategy comprised drilling pilot holes from development wells, which were evaluated using advanced mud gas logs, downhole pressure measurements and wireline/ logging whilst drilling (LWD) logs to confirm fluid distribution and understand connectivity. This paper discusses one such example, where production data from smart wells has been incorporated with appraisal data to optimise well design, placement and production from a thin depleted oil rim. Multi-rate test information has been used to adjust down-hole inflow control valve (ICV) settings, initially to balance drawdown along the horizontal wells and later to minimise water and gas cap production.
Turco, Agostino (Brunei Shell Petroleum Sdn. Bhd.) | Salakhetdinov, Ravil (Brunei Shell Petroleum Sdn. Bhd.) | Dale-Pine, Brigitte (Brunei Shell Petroleum Sdn. Bhd.) | Van Lelden, Robert (Brunei Shell Petroleum Sdn. Bhd.)
This reference is for an abstract only. A full paper was not submitted for this conference.
The current phase of the Champion West Field Development is one of BSP's most challenging project to date for drilling and completion reasons. The Champion West field was discovered in 1975, but its rich oil and gas reserves lay dormant for 30 years, locked beneath the seabed in a complex web of reservoirs deemed too expensive to develop using conventional development plans. It is a large undeveloped oil and gas resource in Brunei. Hydrocarbons are found in shallow marine reservoirs - these vertically stacked, structurally dipping reservoirs are complex and contain various fluid fills ranging from gas only to gas with oil rims to oil. The project consists developing oil rim reservoirs using the novel snake well design and developing of significant gas resources using highly deviated and high-pressured gas wells. Four wells have been drilled to date utilizing advanced drilling technologies and smart-completion equipment to complete over many stacked reservoirs with different pressure regimes, whilst aiming to maximizing ultimate recovery for those sand units. The world first six- zones Smart completion were successfully installed in 2007 with the objectives of realizing increased recovery and a reduction in life cycle cost through real time monitoring and production optimization and a reduction in well intervention as well as reduction in HSE exposure. Challenges associated within the gas project included: Drilling through depleted and virgin pressure regimes of up to 8200 psi; Extended reach drilling of up to 7.3 km; Perforation; and, Installation of Smart-Completions in high-pressured gas environment. In addition to significant oil production, Champion West has been contributing to around 20% of BSP's gas production with a significant proportion coming from these recently drilled gas wells. This paper describes the well concepts adopted and the experiences developed throughout the project in drilling and installing Smart-Completions in this high-pressure gas environment.
Techbits - No abstract available.