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GlassPoint Solar was founded in 2008 to replace the use of natural gas for steamflooding heavy-oil reservoirs. But amid low energy prices, its chief investor has decided to pull the plug for good. This paper demonstrates how maintaining investment in high-quality 3D seismic during the last downturn, together with selective exploration, quality geoscience, application of new technologies, and efficiently maturing discoveries to early cash flow, was successful in sustaining future production. Field N is a complex heavy-oil field in the north of the Sultanate of Oman. The dynamic behavior of Field N is characterized by strong aquifer and is dominated by bottomwater drive.
Green fields today mostly can be regarded as marginal fields and successfully developed. It covers the complete assessment of the oil and gas recovery potential from reservoir structure and formation evaluation, oil and gas reserve mapping, their uncertainties and risks management, feasible reservoir fluid depletion approaches, and to the construction of integrated production systems for cost effective development of the green fields. Depth conversion of time interpretations is a basic skill set for interpreters. There is no single methodology that is optimal for all cases. Next, appropriate depth methods will be presented. Depth imaging should be considered an integral component of interpretation. If the results derived from depth imaging are intended to mitigate risk, the interpreter must actively guide the process.
Africa (Sub-Sahara) Kosmos Energy has made a significant deepwater gas discovery off Senegal. The Guembeul-1 well in the northern part of the St. Louis Offshore Profond license in 8,858 ft of water encountered 331 net ft of gas pay in two excellent-quality reservoirs, the company reported. The results demonstrate reservoir continuity and static pressure communication with the Tortue-1 well, which suggests a single gas accumulation. The mean gross resource estimate for the Greater Tortue complex has risen to 17 Tcf from 14 Tcf as a result of the Guembeul discovery, the company said. Kosmos, the operator, has a 60% interest in the well. Timis (30%) and Petrosen (10%) hold the remaining interest. In Salah Gas has started production from its Southern fields in Algeria.
This seminar covers the fundamental principles concerning how hydraulic fracturing treatments can be used to stimulate oil and gas wells. It includes discussions on how to select wells for stimulation, what controls fracture propagation, fracture width, etc., how to develop data sets, and how to calculate fracture dimensions. The seminar also covers information concerning fracturing fluids, propping agents, and how to design and pump successful fracturing treatments. As the industry wrestles with another price cycle, making sense of the world in which the oil and gas industry will operate is important to understanding the actions (by engineers, corporations, and governments) which must be taken today so that the oil and gas industry may prosper in the future. Hydraulic fracturing has been touted as a ‘new technology’ (though a misnomer) which is opening access to un-tapped value (in the USA) and lowering the cost of energy across the globe by shifting the balance between supply and demand.
Flow zonation and permeability estimation is a common task in reservoir characterization. Typically, integration of openhole log data with a conventional and special core analysis solves this problem. We present a Bayesian-based method for identifying hydraulic flow units in uncored wells using the theory of hydraulic flow units (HFUs) and subsequently compute permeability using wireline log data.
We use a nonlinear optimization scheme on the basis of the probability plot to determine pertinent statistical parameters of each flow unit. Next, we couple these results with the F-test and the Akaike’s criteria with the purpose of establishing the optimal number of HFUs present in the core data set. Then, we allocate the core data into their respective HFUs using the Bayes’ theorem as clustering rule. Finally, we apply an inversion algorithm on the basis of Bayesian inference to predict permeability using only wireline data.
We illustrate the application of the procedure with a carbonate reservoir having extensive conventional core data. The results show that the Bayesian-based clustering and inversion technique delivers permeability estimates that agree with the core data and with the results obtained from a pressure transient analysis.
Traditional geochemical techniques such as pyrolysis and Soxhlet extraction have been used for decades to guide conventional exploration. However, geochemical data obtained in the traditional way falls short of the demands of unconventional reservoir development. We have recently developed innovative analytical and data processing technology that allows geochemical information in the produced oil and rock samples to be captured at a much higher resolution (up to an order of magnitude) and fidelity. These unprecedented geochemical data reveal 1) static reservoir characteristics such as reservoir quality, oil saturation, and 2) dynamic reservoir performance characteristics such as frac growth, drainage height, and inter-well communication. These reservoir characteristics can have impacts throughout the lifecycle of unconventional reservoir development from well stacking & spacing, completion design, reservoir management, and EOR/IOR decisions.
Based on high-resolution/hi-fidelity geochemical fingerprint data, we have developed an integrated workflow to provide new reservoir characterization and production monitoring information that lead directly to enhanced development opportunities for unconventional reservoirs. By data mining the fingerprint data from the rock baseline, a group of Reservoir Characterization Indices (RCI) were developed, including reservoir quality index (RQI) and oil-in-place index (OIPI). Different from other rock-based core analysis, the RCI provided an independent dataset directly from the oil residing in the rock samples. They correlate well with petrophysical data and compliment landing decisions for lateral wells. Produced oil samples were collected from legacy and infill wells. Fingerprints based on over 2000 compounds, resolved from each produced oil sample, were used to reveal well communication through time, as well as quantitative vertical drainage variation against the vertical profile previously established using the core/cutting samples. Integrating the dynamic reservoir monitoring data with the static RCI data, geochemical fingerprinting technology helps operators identify key factors controlling unconventional well performance, such as well spacing, and significantly improves the operator’s ability to predict performance of future development strategies.
Key conclusions from the study include: 1) RCI generated in the workflow conformed well with independent petrophysical analysis; 2) Indications of similar zonal contribution in wells that were landing in different intervals; 3) Drainage geometries in all four landing zones appear to have distinct differences; 4) Distinct overlapped drainage geometries are also evident; 5) Parent wells experience changes in drainage geometry profile post-stimulation of offset child wells, then returned to their established geometry in a relatively short period of time.
Zhao, Haining (China University of Petroleum Beijing) | Jing, Hongbin (China University of Petroleum Beijing) | Fang, Zhengbao (Xinjiang Oilfield Company, PetroChina) | Yu, Hongwei (Research Institute of Petroleum Exploration and Development, PetroChina)
On the basis of a previously published reduced-variables method, we demonstrate that using these reduced variables can substantially accelerate the conventional successive-substitution iterations in solving two-phase flash (TPF) problems. By applying the general dominant eigenvalue method (GDEM) to the successive-substitution iterations in terms of the reduced variables, we obtained a highly efficient solution for the TPF problem. We refer to this solution as Reduced-GDEM. The Reduced-GDEM algorithm is then extensively compared with more than 10 linear-acceleration and Newton-Raphson (NR) -type algorithms. The initial equilibrium ratio for flash calculation is generated from reliable phase-stability analysis (PSA). We propose a series of indicators to interpret the PSA results. Two new insights were obtained from the speed comparison among various algorithms and the PSA. First, the speed and robustness of the Reduced-GDEM algorithm are of the same level as that of the reduced-variables NR flash algorithm, which has previously been proved to be the fastest flash algorithm. Second, two-side phase-stability-analysis results indicate that the conventional successive-substitution phase-stability algorithm is time consuming (but robust) at pressures and temperatures near the stability-test limit locus in the single-phase region and near the spinodal in the two-phase region.
Germik, a mature heavy oil field in Southeast Turkey, has been producing for more than 60 years with a significant decline in pressure and oil production. To predict future performance of this reservoir and explore possible enhanced oil recovery (EOR) scenarios for a better pressure maintenance and improved recovery, generation of a representative dynamic model is required. To address this need, an integrated approach is presented herein for characterization, modeling and history matching of the highly heterogeneous, naturally fractured carbonate reservoir spanning a long production history.
Hydraulic flow unit (HFU) determination is adopted instead of the lithofacies model, not only to introduce more complexity for representing the variances among flow units, but also to establish a higher correlation between porosity and permeability. By means of artificial intelligence (AI), existing wireline logs are used to delineate HFUs in uncored intervals and wells, which is then distributed to the model through stochastic geostatistical methods. A permeability model is subsequently built based on the spatial distribution of HFUs, and different sets of capillary pressures and relative permeability curves are incorporated for each rock type.
The dynamic model is calibrated against the historical production and pressure data through assisted history matching. Uncertain parameters that have the largest impact on the quality of the history match are oil-water contact, aquifer size and strength, horizontal permeability, ratio of vertical to horizontal permeability, capillary pressure and relative permeability curves, which are efficiently and systematically optimized through evolution strategy. Identification and distribution of the hydraulic units complemented with artificial neural networks (ANN) provide a better description of flow zones and a higher confidence permeability model. This reduces uncertainties associated with reservoir characterization and facilitates calibration of the dynamic model. Results obtained from the study show that the history matched simulation model may be used with confidence for testing and optimizing future EOR schemes.
This paper brings a novel approach to permeability and HFU determination based on artificial intelligence, which is especially helpful for addressing uncertainties inherent in highly complex, heterogeneous carbonate reservoirs with limited data. The adopted technique facilitates the calibration of the dynamic model and improves the quality of the history match by providing a better reservoir description through flow unit distinction.
To ensure continued access to JPT's content, please Sign In, JOIN SPE, or Subscribe to JPT Increasing sensitivity to oil price fluctuation requires operators to reduce costs of operation. A key component is collaborative management by operators and suppliers to reduce cost over the full life cycle of the operation. Much of the focus is on the equipment-purchasing strategy; nonetheless, effective technology such as an autonomous inflow control valve could also benefit the operating expenditure (OPEX) savings by enhancing oil recovery and reducing processing costs of excessive unwanted water and gas in surface facilities. OPEX comprises labor, chemicals, consumables, infrastructure maintenance, transportation, disposal, and other utilities. Capital expenditure (CAPEX) may also be reduced for long-term projects, which generally comprise infrastructure such as pipelines, pumps, disposal wells, ponds/storage treatment facilities, and other associated utilities infrastructure.