PRODUCTION FORECAST FOR BAZHEN FORMATION RESERVOIRS ON THE BASIS OF STATISTICAL ANALYSES AND MACHINE LEARNING TECHNIQUES

2020 ◽  
pp. 63-68
Author(s):  
T.N. Shevchuk ◽  
O.Yu. Kashnikov ◽  
M.A. Mezentseva ◽  
I.V. Baykov ◽  
T.S. Karimov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Productivity Index ◽  
Well Performance ◽  
Production Forecast ◽  
Horizontal Section ◽  
Geomechanical Properties ◽  
Technological Factors ◽  
Production Forecasting ◽  
Learning Techniques

The paper reviews actual production results and production forecasting for horizontal wells completed using multistage hydraulic fracturing in Bazhenov formation of Palyan area, Krasnoleninskoye oilfield (HMAO). The key feature of these reservoirs is high degree of uncertainty in geological and geomechanical properties. The primary purpose of this study is to provide both identification of correlation between well productivity index (rates, cumulative production) and general geological-technological factors and software development for production forecasting of Bazhen formation wells. In the course of the works, more than 2000 probable correlations between geological and geomechanical formation properties as well as between technological factors and well performance have been analyzed. Main key complex parameters (length of horizontal section, number of frac stages, average tonnage of proppant and fracturing fluid back-production) that govern producing well performance have been determined in this analysis. The approach for evaluation of forecast well performance (initial rate, cumulative oil production) is provided on the basis of obtained statistical correlations and machine learning techniques. Approaches described in this paper summarize the first results of the phase «Select» in progress of pilot project on Palyan area, Krasnoleninskoye oilfield.

Artificial Intelligence for Infill Well Placement and Design Optimization in Multi-layered/stacked Reservoirs Under Subsurface Uncertainty

2021 ◽  
Author(s):  
Shi Su ◽  
Ralf Schulze-Riegert ◽  
Hussein Mustapha ◽  
Philipp Lang ◽  
Chakib Kada Kloucha
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Multiple Model ◽  
Well Performance ◽  
Reference Case ◽  
Well Placement ◽  
Learning Techniques ◽  
Economic Constraints

Abstract Effective well placement and design planning accounts for subsurface uncertainties to estimate production and economic outcomes. Reservoir modelling and simulation workflows build on ensemble approaches to manage uncertainties for production forecasting. Ensemble generation and interpretation requires a higher degree of automation analytics and artificial intelligence for fast value extraction and decision support. This work develops practical intelligent workflow steps for a robust infill well placement and design scenario in multi-layered/stacked reservoirs under uncertainty. Potential well targets are classified by an opportunity index defined by a combination of rock and hydrocarbon flow properties as well as connected volumes above a minimum economic volume. Unsupervised learning techniques are applied to automate the search for alternative target areas, so-called hotspot regions. Supervised machine/learning models are used to predict infill well performance based on simulated and/or past production experience. A stochastic evaluation including all ensemble cases is used to capture uncertainty. Vertical, deviated, horizontal and multilateral wells are proposed to optimally target single or connect to multiple hotspot regions under technical and economic constraints. A structured workflow design is applied to a multi-layered/stacked reservoir model. Subsurface uncertainties are described and captured by multiple model realizations, which are constrained in areas of historical wells. An infill well program for a multi-layered/stacked reservoir is defined for incremental production increase under economic constraints. This work shows how robust well location and design builds on the full ensemble of cases with a high degree of automation using analytics and machine-learning techniques. Both production and economic targets are calculated and compared to a reference case for robust solution verification and probability of success. In conclusion, an overall reservoir-driven field development strategy is required for efficient execution. However, automation is well applicable to repetitive workflow steps which includes hotspot search in an ensemble of validated reservoir models. This work presents an integrated, intelligent solution for informed decision making on infill drilling locations and refined well design. Higher degree of automation with embedded intelligence are discussed from case generation to hotspot identification. Aspects of model calibration in a producing field environment are addressed.

Machine-Learning Techniques Assist Data-Driven Well-Performance Optimization

2021 ◽  
Vol 73 (10) ◽  
pp. 63-64
Author(s):  
Chris Carpenter
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Performance Optimization ◽  
Production Optimization ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Well Performance ◽  
Learning Techniques ◽  
Expected Performance ◽  
Gas Lift

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 201696, “Robust Data-Driven Well-Performance Optimization Assisted by Machine-Learning Techniques for Natural-Flowing and Gas-Lift Wells in Abu Dhabi,” by Iman Al Selaiti, Carlos Mata, SPE, and Luigi Saputelli, SPE, ADNOC, et al., prepared for the 2020 SPE Annual Technical Conference and Exhibition, originally scheduled to be held in Denver, Colorado, 5–7 October. The paper has not been peer reviewed. Despite being proven to be a cost-effective surveillance initiative, remote monitoring is still not adopted in more than 60% of oil and gas fields around the world. Understanding the value of data through machine-learning (ML) techniques is the basis for establishing a robust surveillance strategy. In the complete paper, the authors develop a data-driven approach, enabled by artificial-intelligence methodologies including ML, to find an optimal operating envelope for gas-lift wells. Real-Time Well-Performance Optimization Wellsite Measurement and Control. - Flow Tests. - Past tests include sporadic measurement of multiphase rates and the associated flowing pressure and temperature, collected at various points of the production system, from bottomhole to separator conditions. Flow tests are also known as well tests; however, the authors use the term “flow test” in this paper to avoid confusion with well testing as used in pressure transient tests, including temporary shut-in pressure buildups (for producers) and pressure falloff tests (for injectors). Normally, a well would have limited data points from the past well tests (i.e., less than 50 valid flow tests in a period of 5–10 years). This data is the basis of creating ML models. Continuous Monitoring. - Every well should have adequate instrumentation, and its supporting infrastructure should include reliable power supply, minimum latency telemetry, and desktop access to production parameters. Alignment among real-time data and relational databases is required. Remote Control and Automated Actuation. - In addition to controllable valves, every well should be enabled with actuators to control the process variables. Remote control allows the operator to make changes to the current well-site configuration. Regulatory and Supervisory Control. - The value of automated closed-loop regulatory and supervisory control is to sustain optimal production while providing high well availability. Real-Time Production Optimization. - Continuous production optimization means that expected performance is challenged frequently by updating an optimal forecast with upper-level targets and current asset status. This is achieved by applying actions that close the gap between actual and expected performance. Faster surveillance loops compare actual vs. expected performance to determine minute, hourly, and daily gaps. A slower surveillance loop updates the asset’s expected performance. Well-Management Guidelines. - These are established, known limits to address and honor restrictions such as concession-contract obligations and legal limits, optimal reservoir management, flow assurance, economics, safety, and integrity.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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