Analysis of Factors of Productivity of Tight Conglomerate Reservoirs Based on Random Forest Algorithm

Abstract

The tight conglomerate reservoir of Baikouquan formation in the MA 131 well block in the Junggar basin abounds with petroleum reserves, yet the vertical wells in this reservoir have achieved a limited development effect. The tight conglomerate reservoirs have become an important target for exploration and exploitation. The high-efficiency development scheme of a small well spacing three-dimensional (3D) staggered well pattern has been determined by a series of field tests on well pattern and well spacing development. Multistage fracturing with a horizontal well has been demonstrated as the primary development technology. The horizontal wells in the MA 131 small well spacing demonstration area have achieved significantly different development effects, and the major controlling factors for high and stable production of a single well remain unclear. In this study, we proposed an evaluation model of major productivity controlling factors of the tight conglomerate reservoir to provide a reference for oil recovery based on a random forest (RF) machine-learning algorithm. The productivity factors were investigated from two aspects: petrophysical facies that are capable of indicating the genetic mechanism of geological dessert and engineering dessert parameters forming complex fracture networks. Resultantly, the reservoir in the MA 131 well block can be classified into 12 petrophysical facies according to the sedimentary characteristics and diagenesis analysis. The mercury injection curves of a variety of petrophysical facies can be classified into four reservoir quality types. The RF model was trained on 80% of the data to predict the oil well class using the selected features as primary inputs while the remaining 20% of the data were set to test the model performance. The results indicated that the RF model produced excellent results with only 12 misclassifications across the entire data set of 627 samples that represent <2% error. The important evaluation score of the random forest algorithm model showed that the reservoir type, oil saturation, horizontal stress difference, and gravel content are the most important four indicators, with each value exceeding 15%. Brittleness and maximum horizontal stress are considered the least important indexes, with values of less than 5%. Reservoir quality and oil saturation were confirmed as the major controlling factors and material foundation for oil wells’ high and stable production. As indicated in this study, stress difference and gravel content are the major controlling factors in the formation of a complex fracture network.

Progress of Crude Oil Gasification Technology Assisted by Microorganisms in Reservoirs

“…Their analysis revealed that when predicting the oil storage rate, the most crucial variables were associated with the size of the oilfield, including cumulative oil production, number of wells, oil production (OIP), and rock volume [151]. In a separate study, Yu et al [152] utilized the random forest (RF) machine-learning algorithm to explore the factors influencing the high and stable production of a single well within a specific well group in the Junggar Basin. Their findings indicated that reservoir quality and oil saturation are the primary controlling factors, establishing the fundamental basis for achieving high and stable oil production [153].…”

A Comparison of Two Machine Learning Techniques for the Prediction of Initial Oil in Place in the Niger Delta Region

“…This consists of a very high potential area in the south of the Bengkalis graben; three locations of high potential areas located on the banks of the Kiri trough and close to the Aman and Balam grabens; two locations of medium potential category II located east and northwest of the Bengkalis graben; and a medium potential area category located east of the Bengkalis graben.The study was limited because no feature relationship was established. Yu et al (2022) analyzed factors of productivity of tight conglomerate reservoirs based on random forest algorithm. The study proposed an evaluation model of major productivity controlling factors of thetight conglomerate reservoir to provide a reference for oil recovery based on a randomforest…”

Water Management and Oil Recovery Improvement Performance of High-Strength Preformed Particle Gels in Fractured Conglomerate Reservoirs

“…In recent years, the development of conglomerate reservoirs using scientific and efficient engineering methods has become a hot topic due to the continuous discovery of large oil and gas resources. − Conglomerate reservoirs exhibit stronger heterogeneity compared to conventional sandstone reservoirs because they are filled with gravels in the matrix. − Additionally, due to the low initial permeability, most of these reservoirs have undergone hydraulic fracturing to enhance the stimulated reservoir volume (SRV). , It makes more oil and gas easy to flow into production wells and be produced. , However, during the following long-term waterflooding stage, injected water will flow along these fractures, and high-conductivity channels form in the reservoir after high-intensity water injection. , Thus, to further exploit the remaining oil of fractured conglomerate reservoirs in the Xinjiang Oilfield, it is urgent to apply efficient water management techniques to enhance water conformance and oil production.…”