HISTORY MATCHING PRESSURE IN THE ARBUCKLE GROUP AQUIFER TO MANAGE MIDCONTINENT SEISMICITY

2019 ◽  
Author(s):  
Esmail Ansari ◽  
◽  
Tandis S. Bidgoli ◽  
Andrew Michael Hollenbach
2019 ◽  
Vol 7 (4) ◽  
pp. SL19-SL36
Author(s):  
Gabriel L. Machado ◽  
Garrett J. Hickman ◽  
Maulin P. Gogri ◽  
Kurt J. Marfurt ◽  
Matthew J. Pranter ◽  
...  

Over the past eight years, north-central Oklahoma has experienced a significant increase in seismicity. Although the disposal of large volumes of wastewater into the Arbuckle Group basement system has been statistically correlated to this increased seismicity, our understanding of the actual mechanisms involved is somewhat superficial. To address this shortcoming, we initiated an integrated study to characterize and model the Arbuckle-basement system to increase our understanding of the subsurface dynamics during the wastewater-disposal process. We constructed a 3D geologic model that integrates 3D seismic data, well logs, core measurements, and injection data. Poststack-data conditioning and seismic attributes provided images of faults and the rugose top of the basement, whereas a modified-Hall analysis provided insights into the injection behavior of the wells. Using a Pareto-based history-matching technique, we calibrated the 3D models using the injection rate and pressure data. The history-matching process showed the dominant parameters to be formation-water properties, permeability, porosity, and horizontal anisotropy of the Arbuckle Group. Based on the pressure buildup responses from the calibrated models, we identified sealing and conductive characteristics of the key faults. Our analysis indicates the average porosity and permeability of Arbuckle Group to be approximately 7% and 10 mD, respectively. The simulation models also showed pockets of nonuniform and large pressure buildups in these formations, indicating that faults play an important role in fluid movement within the Arbuckle Group basement system. As one of the first integrated investigations conducted to understand the potential hydraulic coupling between the Arbuckle Group and the underlying basement, we evaluate the need for improved data recording and additional data collection. In particular, we recommend that operators wishing to pursue this type of analysis record their injection data on a daily rather than on an averaged basis. A more quantitative estimation of reservoir properties requires the acquisition of P-wave and dipole sonic logs in addition to the commonly acquired triple-combo logs. Finally, to better quantify flow units with the disposal reservoir, we recommend that operators acquire sufficient core to characterize the reservoir heterogeneity.


2014 ◽  
Author(s):  
G. A. Carvajal ◽  
M. Maucec ◽  
A. Singh ◽  
A. Mahajan ◽  
J. Dhar ◽  
...  

2014 ◽  
Author(s):  
Dennis Chinedu Obidegwu ◽  
Romain Louis Chassagne ◽  
Colin Macbeth

2015 ◽  
Vol 4 (2) ◽  
pp. 44-52
Author(s):  
Novia Rita ◽  
Tomi Erfando

Sebelum suatu model reservoir digunakan, terlebih dahulu dilakukan history matching atau menyesuaikan kondisi model dengan dengan kondisi reservoir. Salah satu parameter yang perlu disesuaikan adalah permeabilitas relatif. Untuk melakukan rekonstruksi nilai permeabilitas relatifnya dibutuhkan data SCAL (Special Core Analysis) dari sampel core. Langkah awal rekonstruksi adalah dengan melakukan normalisasi data permeabilitas relatif (kr) dan saturasi air (Sw) dari data SCAL yang berasal dari tiga sampel core. Setelah dilakukan nomalisasi, dilakukan denormalisasi data permeabilitas relatif yang akan dikelompokkan berdasarkan jenis batuannya. Setelah dilakukan history matching menggunakan black oil simulator, data denormalisasi tersebut belum sesuai dengan kondisi aktual. Selanjutnya digunakan persamaan Corey untuk rekonstruksi kurva permeabilitas relatifnya. Hasil dari persamaan tersebut didapat bahwa nilai kro dan krw jenis batuan 1 sebesar 0,25 dan 0,09 kemudian nilai kro dan krw untuk jenis batuan 2 sebesar 0,4 dan 0,2. Nilai permeabilitas dari persamaan Corey digunakan untuk melakukan history matching, hasilnya didapat kecocokan (matching) dengan keadaan aktual. Berdasarkan hasil simulasi, nilai produksi minyak aktualnya adalah 1.465.650 bbl sedangkan produksi dari simulasi adalah 1.499.000 bbl. Artinya persentase perbandingan aktual dan simulasinya adalah 1,14% yang dapat dikatakan cocok karena persentase perbedaannya di bawah 5%.


Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1055
Author(s):  
Qian Sun ◽  
William Ampomah ◽  
Junyu You ◽  
Martha Cather ◽  
Robert Balch

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.


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