Copyright 2019 held jointly by the Society of Petrophysicists and Well Log Analysts (SPWLA) and the submitting authors. ABSTRACT Today, many machine learning techniques are regularly employed in petrophysical modelling such as cluster analysis, neural networks, fuzzy logic, self-organising maps, genetic algorithm, principal component analysis etc. While each of these methods has its strengths and weaknesses, one of the challenges to most of the existing techniques is how to best handle the variety of dynamic ranges present in petrophysical input data. Mixing input data with logarithmic variation (such as resistivity) and linear variation (such as gamma ray) while effectively balancing the weight of each variable can be particularly difficult to manage. DTA is conceived based on extensive research conducted in the field of CFD (Computational Fluid Dynamics). This paper is focused on the application of DTA to petrophysics and its fundamental distinction from various other statistical methods adopted in the industry. Case studies are shown, predicting porosity and permeability for a variety of scenarios using the DTA method and other techniques. The results from the various methods are compared, and the robustness of DTA is illustrated. The example datasets are drawn from public databases within the Norwegian and Dutch sectors of the North Sea, and Western Australia, some of which have a rich set of input data including logs, core, and reservoir characterisation from which to build a model, while others have relatively sparse data available allowing for an analysis of the effectiveness of the method when both rich and poor training data are available. The paper concludes with recommendations on the best way to use DTA in real-time to predict porosity and permeability. INTRODUCTION The seismic shift in the data analytics landscape after the Macondo disaster has produced intensive focus on the accuracy and precision of prediction of pore pressure and petrophysical parameters.
The Slootdorp field has a complex structure with most reserves in Rotliegend sandstone, which is communicating with gas bearing Zechstein carbonates. The Rotliegend reservoir is bounded by a large fault, which might become seismogenic during depletion. A 3D geomechanical model was built, based on the faults and horizons in the geological model. Both the Rotliegend and Zechstein reservoirs were included in the model. The model was populated with geomechanical properties derived from logs, LOT's (leak off tests) and regional data on the stress field. Also, overburden properties from previous studies on nearby fields were used.
The pressure input was obtained from reservoir simulation. It is important to include the water leg pressure in the pressure input since the Rotliegend gas reservoir is in contact with an active aquifer. Pressure reduction drives the compaction of the reservoir, which induces stresses on the faults causing slippage. Since the water is quite incompressible, a large pressure reduction in the water leg may be caused temporarily by a rising gas water contact.
It turned out that slippage is not expected at the lowest gas pressure using a conservative estimate of the critical friction coefficient on the fault of 0.55. Sensitivity analysis on the most important input parameters was performed with a range that can be expected for such a field. The result was that the maximum critical stress ratio could range between 0.46 and 0.53 for the expected uncertainty of input parameters. The geomechanical modeling shows that an active aquifer has a dominant, mitigating effect on seismic risk, which can explain why many reservoirs show no seismicity in the Netherlands, although other effects could also play a role.
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.
With the purchase, the growing, privately-held Chrysaor Holdings will expand its UK North Sea production to 185,000 BOE/D. The state-run offshore company has found a gas and condensate field that holds an estimated 250 million BOE. The latest example of the offshore sector's march toward automated wellbore construction will take shape later this year in the North Sea. Just 2 months after issuing more than a hundred licenses, the Oil and Gas Authority begins the process again for a whole new set of blocks. The company announced it would “initiate the process” of marketing its UK Central North Sea fields as part of a portfolio review.
Africa (Sub-Sahara) Bowleven began drilling operations at its Zingana exploration well on the Bomono permit in Cameroon. Located 20 km northwest of Douala, Cameroon's largest city, the well will target a Paleocene (Tertiary) aged, three-way dip closed fault block. The company plans to drill the well to a depth of 2000 m and will then spud a second well in Moambe, 2 km east of Zingana. Bowleven is the operator and holds 100% interest in the license. Asia Pacific China National Offshore Oil Company (CNOOC) has brought its Dongfang 1-1 gas field Phase I adjustment project on line ahead of schedule. The field is located in the Yinggehai basin of the Beibu Gulf in the South China Sea and has an average water depth of 70 m. The field is currently producing 53 MMcf/D of gas and is expected to reach its peak production of 54 MMcf/D before the end of the year.
Hurlburt, Maurice (Athabasca Oil Corp.) | Quintero, Jonathan (Baker Hughes, a GE Company) | Bradshaw, Robert (Baker Hughes, a GE Company) | Belloso, Andres (Baker Hughes, a GE Company) | Cripps, Evan (Baker Hughes, a GE Company) | Blakney, Donya (Baker Hughes, a GE Company) | Glass, Darnell (Baker Hughes, a GE Company)
A Canadian oil & gas operator has been setting new benchmarks drilling the vertical and tangent section of Montney horizontal wells in the Placid field of Northern Alberta. Initially, the operator drilled vertical wells to kick off point (KOP) with polycrystalline diamond compacts (PDC) and conventional mud motors. As a result of increasing well density, however, the well plans consistently required a 15° to 30° tangent section. With PDC drilling, toolface and build up rates were problematic and the sliding rate of penetration (ROP) was slow.
A Rotary Steerable System (RSS) was introduced, but despite the improved performance, the technology came at a premium cost and the severity of drilling dysfunctions generated an increase in tool failures. With falling oil prices, a more cost effective solution was required.
Hybrid bit technology, which combines the cutting mechanism of both fixed cutter and roller-cone bits, has been extensively utilized in Canada to drill build sections, providing outstanding results. They have not, however, been commonly used to drill the vertical (drill-out) and tangent sections. The operator combined a state-of-the-art hybrid bit with a mud motor to drill the interval with an 85% success rate. The combination of the hybrid bit and conventional motor, compared to PDC and RSS, resulted in a 30% cost savings to complete the interval.
The present case study outlines how hybrid bit technology development, driven by field data in a continuous improvement cycle, identifies performance opportunities, which have a significant impact on drilling time and cost savings in drill out sections. The overall objective of this current case study is to highlight the results and lessons learned throughout the implementation process.
Various novel computing architectures, like massively parallel and multi-core, as well as computing accelerators, like GPUs or TPUs, keep regularly expanding. In order to exploit the benefits of these architectures to the full extent and speed up reservoir simulation, the source code has to be inevitably rewritten, sometimes almost completely. We demonstrate how to extract complex physicsrelated computations from the main simulation loop, leaving only an algebraic multilinear interpolation kernel instead. In combination with linear solvers, which usually have made available soon once the new architecture is introduced, the approach accommodates execution of the entire nonlinear loop on the latest hardware and computational architectures. We describe the integrated simulation framework built on top of this technique and show the applicability of the approach to various challenging physical and chemical problems. All simulation engines along with linear solvers, well controls, interpolation engines, and state operator evaluators are implemented in C 11 and exposed into Python coupling the flexibility of the script language with the performance of C .
This course will discuss the practical state-of-the-art techniques of Volume to Value (VV) to help attendees assess exploratory deepwater offshore oil and gas prospects and quantify economic values of the prospects. Attendees will learn how to develop a preliminary field development plan for a given discovery prospect and estimate oil and gas recovery, wells required, and costs. They will also learn how to conduct economic evaluation for lease sales or farm-in opportunities. Upon completion of this course, attendees should be able to evaluate the commercial potential of original oil and gas in-place in exploratory blocks and develop preliminary field development plans. Attendees should also be able to obtain value of the opportunity in order to make the decision to go ahead and develop the field or walk away from it, as well as identify constraints in terms of geology and engineering that will make it viable or impede the realization of the project.